1 public class Convolution extends Thread {
5 public Convolution(Image img, int x0, int x1, int y0, int y1) {
17 double[][] kernel = new double[kernelHeight][kernelWidth];
20 double tempinput[][] = img.inputImage;
21 double tempout[][] = img.outputImage;
23 double tinput1[] = tempinput[x0];
24 double tinput2[] = tempinput[x0+1];
25 double tinput3[] = tempinput[x0+2];
26 double tinput4[] = tempinput[x0+3];
27 double tinput5[] = tempinput[x0+4];
28 double tinput6[] = tempinput[x0+5];
29 double tinput7[] = tempinput[x0+6];
30 double tinput8[] = tempinput[x0+7];
31 double tinput9[] = tempinput[x0+8];
32 double tinput10[] = tempinput[x0+9];
33 double tinput11[] = tempinput[x0+10];
34 double tinput12[] = tempinput[x0+11];
35 double tinput13[] = tempinput[x0+12];
36 double tinput14[] = tempinput[x0+13];
37 double tinput0[] = tinput1;
40 for(int i=x0;i<x1;i++,l++){
41 double tout[] = tempout[i];
42 tinput0 = tinput1; tinput1=tinput2; tinput2=tinput3; tinput3=tinput4; tinput4=tinput5;
43 tinput5 = tinput6; tinput6=tinput7; tinput7=tinput8; tinput8=tinput9; tinput9=tinput10;
44 tinput10 = tinput11; tinput11=tinput12; tinput12=tinput13; tinput13=tinput14; tinput14=tempinput[l];
45 for(int j=y0;j<y1;++j){
47 for(int b=0;b<kernelHeight;++b) {
48 s+=(tinput0[j+b] * kernel[0][b] + tinput1[j+b] * kernel[1][b] + tinput2[j+b]*kernel[2][b]+
49 tinput3[j+b]*kernel[3][b] + tinput4[j+b]*kernel[4][b] + tinput5[j+b]*kernel[5][b]+
50 tinput6[j+b]*kernel[6][b] + tinput7[j+b]*kernel[7][b] + tinput8[j+b]*kernel[8][b]+
51 tinput9[j+b]*kernel[9][b] + tinput10[j+b]*kernel[10][b] + tinput11[j+b]*kernel[11][b]+
52 tinput12[j+b]*kernel[12][b]+ tinput13[j+b]*kernel[13][b] + tinput14[j+b]*kernel[14][b]);
59 public static void main(String[] args) {
62 int kernelHeight=15, kernelWidth=15;
65 NUM_THREADS = Integer.parseInt(args[0]);
67 SIZE = Integer.parseInt(args[1]);
75 img = new Image(SIZE,SIZE,kernelHeight,kernelWidth);
77 conv = new Convolution[NUM_THREADS];
78 int increment=SIZE/NUM_THREADS;
80 for(int i = 0; i<NUM_THREADS; i++) {
81 if((i+1)==NUM_THREADS)
82 conv[i] = new Convolution(img, base, SIZE, 0, SIZE);
84 conv[i] = new Convolution(img, base, base+increment, 0, SIZE);
89 System.printString("img.outputImage[10][20] = " +(int) img.outputImage[10][20] + "\n");
90 System.printString("img.outputImage[256][890] = " +(int) img.outputImage[256][890] + "\n");
93 System.printString("Convolution: Size=");
94 System.printInt(SIZE);
95 System.printString("\n");
97 for(int i = 0; i <NUM_THREADS; i++) {
103 System.printString("img.outputImage[10][20] = " +(int) img.outputImage[10][20] + "\n");
104 System.printString("img.outputImage[256][890] = " +(int) img.outputImage[256][890] + "\n");
106 System.printString("2DConv Done!\n");
109 //define 15X15 Gaussian kernel
110 public static void initKernel15(double[][] kernel) {
111 kernel[0][0] = 1/256.0;
112 kernel[0][1] = 4/256.0;
113 kernel[0][2] = 6/256.0;
114 kernel[0][3] = 8/256.0;
115 kernel[0][4] = 10/256.0;
116 kernel[0][5] = 12/256.0;
117 kernel[0][6] = 14/256.0;
118 kernel[0][7] = 16/256.0;
119 kernel[0][8] = 14/256.0;
120 kernel[0][9] = 12/256.0;
121 kernel[0][10] = 10/256.0;
122 kernel[0][11] = 8/256.0;
123 kernel[0][12] = 6/256.0;
124 kernel[0][13] = 4/256.0;
125 kernel[0][14] = 1/256.0;
127 kernel[1][0] = 4/256.0;
128 kernel[1][1] = 16/256.0;
129 kernel[1][2] = 24/256.0;
130 kernel[1][3] = 32/256.0;
131 kernel[1][4] = 40/256.0;
132 kernel[1][5] = 48/256.0;
133 kernel[1][6] = 56/256.0;
134 kernel[1][7] = 64/256.0;
135 kernel[1][8] = 56/256.0;
136 kernel[1][9] = 48/256.0;
137 kernel[1][10] = 40/256.0;
138 kernel[1][11] = 32/256.0;
139 kernel[1][12] = 24/256.0;
140 kernel[1][13] = 16/256.0;
141 kernel[1][14] = 4/256.0;
143 kernel[2][0] = 6/256.0;
144 kernel[2][1] = 24/256.0;
145 kernel[2][2] = 36/256.0;
146 kernel[2][3] = 48/256.0;
147 kernel[2][4] = 60/256.0;
148 kernel[2][5] = 72/256.0;
149 kernel[2][6] = 84/256.0;
150 kernel[2][7] = 96/256.0;
151 kernel[2][8] = 84/256.0;
152 kernel[2][9] = 72/256.0;
153 kernel[2][10] = 60/256.0;
154 kernel[2][11] = 48/256.0;
155 kernel[2][12] = 36/256.0;
156 kernel[2][13] = 24/256.0;
157 kernel[2][14] = 6/256.0;
159 kernel[3][0] = 8/256.0;
160 kernel[3][1] = 32/256.0;
161 kernel[3][2] = 48/256.0;
162 kernel[3][3] = 64/256.0;
163 kernel[3][4] = 80/256.0;
164 kernel[3][5] = 96/256.0;
165 kernel[3][6] = 112/256.0;
166 kernel[3][7] = 128/256.0;
167 kernel[3][8] = 112/256.0;
168 kernel[3][9] = 96/256.0;
169 kernel[3][10] = 80/256.0;
170 kernel[3][11] = 64/256.0;
171 kernel[3][12] = 48/256.0;
172 kernel[3][13] = 32/256.0;
173 kernel[3][14] = 8/256.0;
176 kernel[4][0] = 10/256.0;
177 kernel[4][1] = 40/256.0;
178 kernel[4][2] = 60/256.0;
179 kernel[4][3] = 80/256.0;
180 kernel[4][4] = 100/256.0;
181 kernel[4][5] = 120/256.0;
182 kernel[4][6] = 140/256.0;
183 kernel[4][7] = 160/256.0;
184 kernel[4][8] = 140/256.0;
185 kernel[4][9] = 120/256.0;
186 kernel[4][10] = 100/256.0;
187 kernel[4][11] = 80/256.0;
188 kernel[4][12] = 60/256.0;
189 kernel[4][13] = 40/256.0;
190 kernel[4][14] = 10/256.0;
192 kernel[5][0] = 12/256.0;
193 kernel[5][1] = 48/256.0;
194 kernel[5][2] = 72/256.0;
195 kernel[5][3] = 96/256.0;
196 kernel[5][4] = 120/256.0;
197 kernel[5][5] = 144/256.0;
198 kernel[5][6] = 168/256.0;
199 kernel[5][7] = 192/256.0;
200 kernel[5][8] = 168/256.0;
201 kernel[5][9] = 144/256.0;
202 kernel[5][10] = 120/256.0;
203 kernel[5][11] = 96/256.0;
204 kernel[5][12] = 72/256.0;
205 kernel[5][13] = 48/256.0;
206 kernel[5][14] = 12/256.0;
208 kernel[6][0] = 14/256.0;
209 kernel[6][1] = 56/256.0;
210 kernel[6][2] = 84/256.0;
211 kernel[6][3] = 112/256.0;
212 kernel[6][4] = 140/256.0;
213 kernel[6][5] = 168/256.0;
214 kernel[6][6] = 196/256.0;
215 kernel[6][7] = 224/256.0;
216 kernel[6][8] = 196/256.0;
217 kernel[6][9] = 168/256.0;
218 kernel[6][10] = 140/256.0;
219 kernel[6][11] = 112/256.0;
220 kernel[6][12] = 84/256.0;
221 kernel[6][13] = 56/256.0;
222 kernel[6][14] = 14/256.0;
224 kernel[7][0] = 16/256.0;
225 kernel[7][1] = 64/256.0;
226 kernel[7][2] = 96/256.0;
227 kernel[7][3] = 128/256.0;
228 kernel[7][4] = 160/256.0;
229 kernel[7][5] = 192/256.0;
230 kernel[7][6] = 224/256.0;
231 kernel[7][7] = 256/256.0;
232 kernel[7][8] = 224/256.0;
233 kernel[7][9] = 192/256.0;
234 kernel[7][10] = 160/256.0;
235 kernel[7][11] = 128/256.0;
236 kernel[7][12] = 96/256.0;
237 kernel[7][13] = 64/256.0;
238 kernel[7][14] = 16/256.0;
240 kernel[8][0] = 14/256.0;
241 kernel[8][1] = 56/256.0;
242 kernel[8][2] = 84/256.0;
243 kernel[8][3] = 112/256.0;
244 kernel[8][4] = 140/256.0;
245 kernel[8][5] = 168/256.0;
246 kernel[8][6] = 196/256.0;
247 kernel[8][7] = 224/256.0;
248 kernel[8][8] = 196/256.0;
249 kernel[8][9] = 168/256.0;
250 kernel[8][10] = 140/256.0;
251 kernel[8][11] = 112/256.0;
252 kernel[8][12] = 84/256.0;
253 kernel[8][13] = 56/256.0;
254 kernel[8][14] = 14/256.0;
256 kernel[9][0] = 12/256.0;
257 kernel[9][1] = 48/256.0;
258 kernel[9][2] = 72/256.0;
259 kernel[9][3] = 96/256.0;
260 kernel[9][4] = 120/256.0;
261 kernel[9][5] = 144/256.0;
262 kernel[9][6] = 168/256.0;
263 kernel[9][7] = 192/256.0;
264 kernel[9][8] = 168/256.0;
265 kernel[9][9] = 144/256.0;
266 kernel[9][10] = 120/256.0;
267 kernel[9][11] = 96/256.0;
268 kernel[9][12] = 72/256.0;
269 kernel[9][13] = 48/256.0;
270 kernel[9][14] = 12/256.0;
272 kernel[10][0] = 10/256.0;
273 kernel[10][1] = 40/256.0;
274 kernel[10][2] = 60/256.0;
275 kernel[10][3] = 80/256.0;
276 kernel[10][4] = 100/256.0;
277 kernel[10][5] = 120/256.0;
278 kernel[10][6] = 140/256.0;
279 kernel[10][7] = 160/256.0;
280 kernel[10][8] = 140/256.0;
281 kernel[10][9] = 120/256.0;
282 kernel[10][10] = 100/256.0;
283 kernel[10][11] = 80/256.0;
284 kernel[10][12] = 60/256.0;
285 kernel[10][13] = 40/256.0;
286 kernel[10][14] = 10/256.0;
288 kernel[11][0] = 8/256.0;
289 kernel[11][1] = 32/256.0;
290 kernel[11][2] = 48/256.0;
291 kernel[11][3] = 64/256.0;
292 kernel[11][4] = 80/256.0;
293 kernel[11][5] = 96/256.0;
294 kernel[11][6] = 112/256.0;
295 kernel[11][7] = 128/256.0;
296 kernel[11][8] = 112/256.0;
297 kernel[11][9] = 96/256.0;
298 kernel[11][10] = 80/256.0;
299 kernel[11][11] = 64/256.0;
300 kernel[11][12] = 48/256.0;
301 kernel[11][13] = 32/256.0;
302 kernel[11][14] = 8/256.0;
304 kernel[12][0] = 6/256.0;
305 kernel[12][1] = 24/256.0;
306 kernel[12][2] = 36/256.0;
307 kernel[12][3] = 48/256.0;
308 kernel[12][4] = 60/256.0;
309 kernel[12][5] = 72/256.0;
310 kernel[12][6] = 84/256.0;
311 kernel[12][7] = 96/256.0;
312 kernel[12][8] = 84/256.0;
313 kernel[12][9] = 72/256.0;
314 kernel[12][10] = 60/256.0;
315 kernel[12][11] = 48/256.0;
316 kernel[12][12] = 36/256.0;
317 kernel[12][13] = 24/256.0;
318 kernel[12][14] = 6/256.0;
320 kernel[13][0] = 4/256.0;
321 kernel[13][1] = 16/256.0;
322 kernel[13][2] = 24/256.0;
323 kernel[13][3] = 32/256.0;
324 kernel[13][4] = 40/256.0;
325 kernel[13][5] = 48/256.0;
326 kernel[13][6] = 56/256.0;
327 kernel[13][7] = 64/256.0;
328 kernel[13][8] = 56/256.0;
329 kernel[13][9] = 48/256.0;
330 kernel[13][10] = 40/256.0;
331 kernel[13][11] = 32/256.0;
332 kernel[13][12] = 24/256.0;
333 kernel[13][13] = 16/256.0;
334 kernel[13][14] = 4/256.0;
336 kernel[14][0] = 1/256.0;
337 kernel[14][1] = 4/256.0;
338 kernel[14][2] = 6/256.0;
339 kernel[14][3] = 8/256.0;
340 kernel[14][4] = 10/256.0;
341 kernel[14][5] = 12/256.0;
342 kernel[14][6] = 14/256.0;
343 kernel[14][7] = 16/256.0;
344 kernel[14][8] = 14/256.0;
345 kernel[14][9] = 12/256.0;
346 kernel[14][10] = 10/256.0;
347 kernel[14][11] = 8/256.0;
348 kernel[14][12] = 6/256.0;
349 kernel[14][13] = 4/256.0;
350 kernel[14][14] = 1/256.0;
356 int kernelWidth, kernelHeight;
357 double[][] inputImage;
358 double[][] outputImage;
360 public Image(int width, int height, int kernelWidth, int kernelHeight) {
362 this.height = height;
363 this.kernelWidth = kernelWidth;
364 this.kernelHeight = kernelHeight;
365 inputImage = new double[height+kernelHeight-1][width+kernelWidth-1];
366 outputImage = new double[height][width];
369 /* Create a valid image */
370 public void setValues() {
371 for (int i = 0; i < (height+kernelHeight - 1); i++) {
372 double ainput[] = inputImage[i];
373 for(int j = 0; j < (width+kernelWidth - 1); j++) {
378 for (int i = 0; i < height; i++){
379 double aout[] = outputImage[i];
380 for(int j = 0; j < width; j++) {