0.6 Signal processing in processing: elementary filters  (Page 3/3)

//filtra = new functionvoid filtra(float[] DATAF, float[]DATA, float a0, float a1) { for(int i = 3; i<DATA.length; i++){ DATAF[i]= a0*DATA[i]+a1*DATA[i-1]+a0*DATA[i-2];//Symmetric FIR filter of the second order} }

By writing a for loop that repeats the filtering operation a certain number of times, one canverify that the effect of filtering is emphasized. This intuitive result is due to the fact that, as far as thesignal is concerned, going through $m$ filters of order $N$ (in our case $N=2$ ) is equivalent to going through a single filter of order $mN$

// smoothing Gaussian filter, adapted from REAS: // http://processing.org/learning/topics/blur.htmlsize(200, 200); PImage a; // Declare variable "a" of type PImagea = loadImage("vetro.jpg"); // Load the images into the program image(a, 0, 0); // Displays the image from point (0,0)int n2 = 5/2;int m2 = 5/2; int[][] output = new int[width][height];float[][]kernel = { {1, 4, 7, 4, 1}, {4, 16, 26, 16, 4},{7, 26, 41, 26, 7}, {4, 16, 26, 16, 4},{1, 4, 7, 4, 1} };for (int i=0; i<5; i++) for (int j=0; j<5; j++) kernel[i][j] = kernel[i][j]/273;// Convolve the image for(int y=0; y<height; y++) { for(int x=0; x<width/2; x++) { float sum = 0;for(int k=-n2; k<=n2; k++) { for(int j=-m2; j<=m2; j++) { // Reflect x-j to not exceed array boundaryint xp = x-j; int yp = y-k;if (xp<0) { xp = xp + width;} else if (x-j>= width) { xp = xp - width;} // Reflect y-k to not exceed array boundaryif (yp<0) { yp = yp + height;} else if (yp>= height) { yp = yp - height;} sum = sum + kernel[j+m2][k+n2] * red(get(xp, yp));} }output[x][y]= int(sum); }}// Display the result of the convolution // by copying new data into the pixel bufferloadPixels(); for(int i=0; i<height; i++) { for(int j=0; j<width/2; j++) { pixels[i*width + j]= color(output[j][i], output[j][i], output[j][i]); }} updatePixels();

Median filter: // smoothed_glass// smoothing filter, adapted from REAS: // http://www.processing.org/learning/examples/blur.htmlsize(210, 170); PImage a; // Declare variable "a" of type PImagea = loadImage("vetro.jpg"); // Load the images into the program image(a, 0, 0); // Displays the image from point (0,0)// corrupt the central strip of the image with random noisefloat noiseAmp = 0.1; loadPixels();for(int i=0; i<height; i++) { for(int j=width/4; j<width*3/4; j++) { int rdm = constrain((int)(noiseAmp*random(-255, 255) +red(pixels[i*width + j])), 0, 255);pixels[i*width + j] = color(rdm, rdm, rdm);} }updatePixels();int[][]output = new int[width][height]; int[]sortedValues = {0, 0, 0, 0, 0}; int grayVal;// Convolve the imagefor(int y=0; y<height; y++) { for(int x=0; x<width/2; x++) { int indSort = 0;for(int k=-1; k<=1; k++) { for(int j=-1; j<=1; j++) { // Reflect x-j to not exceed array boundaryint xp = x-j; int yp = y-k;if (xp<0) { xp = xp + width;} else if (x-j>= width) { xp = xp - width;} // Reflect y-k to not exceed array boundaryif (yp<0) { yp = yp + height;} else if (yp>= height) { yp = yp - height;} if ((((k != j)&&(k != (-j))) ) || (k == 0)) { //cross selection grayVal = (int)red(get(xp, yp));indSort = 0; while (grayVal<sortedValues[indSort]) {indSort++; }for (int i=4; i>indSort; i--) sortedValues[i] = sortedValues[i-1]; sortedValues[indSort]= grayVal; }} }output[x][y]= int(sortedValues[2]);for (int i=0; i<5; i++) sortedValues[i] = 0;} }// Display the result of the convolution// by copying new data into the pixel buffer loadPixels();for(int i=0; i<height; i++) { for(int j=0; j<width/2; j++) { pixels[i*width + j]= color(output[j][i], output[j][i], output[j][i]);} }updatePixels();