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tonussi/Sobel.java

Last active December 17, 2015 02:39
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Java code for image border detection
Raw
output1
before sobel
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 1 1 0 0 0
0 0 0 1 1 0 0 0 0 0
0 0 1 1 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
after sobel
0 0 0 0 0 0 0 0 0 0
0 0 0 2 4 4 4 2 0 0
0 0 2 6 4 2 2 2 0 0
0 2 6 4 4 6 4 2 0 0
0 4 4 4 6 2 0 0 0 0
0 4 2 6 2 0 0 0 0 0
0 4 0 4 0 0 0 0 0 0
0 4 0 4 0 0 0 0 0 0
0 4 0 4 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
before convolution
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 1 1 0 0 0
0 0 0 1 1 0 0 0 0 0
0 0 1 1 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
after convolution gx
0 0 1 1 0 -1 -1 0
0 1 3 1 -1 -2 -2 0
1 3 2 -2 -2 -1 -1 0
3 3 -2 -3 -1 0 0 0
4 1 -4 -1 0 0 0 0
4 0 -4 0 0 0 0 0
4 0 -4 0 0 0 0 0
4 0 -4 0 0 0 0 0
after convolution gy
0 0 1 3 4 3 1 0
0 1 3 3 1 0 0 0
1 3 2 -2 -4 -3 -1 0
1 1 -2 -3 -1 0 0 0
0 -1 -2 -1 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
Raw
output2
before sobel
1 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 1
after sobel
0 0 0 0 0 0 0 0 0 0
0 0 4 2 0 0 0 0 0 0
0 4 0 4 2 0 0 0 0 0
0 2 4 0 4 2 0 0 0 0
0 0 2 4 0 4 2 0 0 0
0 0 0 2 4 0 4 2 0 0
0 0 0 0 2 4 0 4 2 0
0 0 0 0 0 2 4 0 4 0
0 0 0 0 0 0 2 4 0 0
0 0 0 0 0 0 0 0 0 0
before convolution
1 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 1
after convolution gx
0 -2 -1 0 0 0 0 0
2 0 -2 -1 0 0 0 0
1 2 0 -2 -1 0 0 0
0 1 2 0 -2 -1 0 0
0 0 1 2 0 -2 -1 0
0 0 0 1 2 0 -2 -1
0 0 0 0 1 2 0 -2
0 0 0 0 0 1 2 0
after convolution gy
0 2 1 0 0 0 0 0
-2 0 2 1 0 0 0 0
-1 -2 0 2 1 0 0 0
0 -1 -2 0 2 1 0 0
0 0 -1 -2 0 2 1 0
0 0 0 -1 -2 0 2 1
0 0 0 0 -1 -2 0 2
0 0 0 0 0 -1 -2 0
Raw
Sobel.java
import static java.lang.Math.abs;
import static java.lang.System.out;
public class Sobel {
public static void main(String[] args) {
int nroLinhas = 10, nroColunas = 10;
int[][] iRef, g, gx, gy;
iRef = new int[nroLinhas][nroColunas];
g = new int[nroLinhas][nroColunas];
gx = new int[3][3];
gy = new int[3][3];
Sobel.fill(iRef);
Sobel.fill(g);
Sobel.kernel(gx, new int[][] { { +1, 0, -1 }, { +2, 0, -2 },
{ +1, 0, -1 } });
Sobel.kernel(gy, new int[][] { { +1, +2, +1 }, { 0, 0, 0 },
{ -1, -2, -1 } });
int[][] foo = Sobel.convolution2DPadded(g, nroLinhas, nroColunas, gx,
3, 3);
int[][] bar = Sobel.convolution2DPadded(g, nroLinhas, nroColunas, gy,
3, 3);
Sobel.display(iRef);
out.println();
Sobel.display(g);
out.println();
Sobel.display(gx);
out.println();
Sobel.display(gy);
out.println();
Sobel.display(foo);
out.println();
Sobel.display(bar);
out.println();
Sobel.display(Sobel.g(foo, bar));
}
public static int[][] convolutionG(int[][] d_reg_matriz_conv_gx,
int[][] d_reg_matriz_conv_gy, int reg_input_n) {
int[][] d_reg_convolucao = new int[reg_input_n][reg_input_n];
for (int i = 0; i < reg_input_n; i++)
for (int j = 0; j < reg_input_n; j++)
d_reg_convolucao[i][j] = Math.abs(d_reg_matriz_conv_gx[i][j])
+ Math.abs(d_reg_matriz_conv_gy[i][j]);
return d_reg_convolucao;
}
public static void kernel(int[][] mask, int[][] operator) {
assert (mask.length == mask[0].length || mask == null) : "InvalidMaskException"
+ (mask.length == mask[0].length);
assert (operator.length == operator[0].length || operator == null) : "BadOperatorException"
+ (mask.length == mask[0].length);
for (int i = 0; i < operator.length; i++)
for (int j = 0; j < operator[i].length; j++)
mask[i][j] = operator[i][j];
}
public static void display(int[] inputArray1D) {
assert (inputArray1D.length == inputArray1D.length || inputArray1D == null) : "BadInputArrayException"
+ (inputArray1D.length == inputArray1D.length);
for (int i = 0; i < inputArray1D.length; i++)
out.println(inputArray1D[i]);
}
public static void display(int[][] inputArray2D) {
assert (inputArray2D.length == inputArray2D[0].length || inputArray2D == null) : "BadInputArrayException"
+ (inputArray2D.length == inputArray2D[0].length);
for (int i = 0; i < inputArray2D.length; i++) {
for (int j = 0; j < inputArray2D[i].length; j++) {
if (inputArray2D[i][j] < 0)
out.print(" " + inputArray2D[i][j] + " ");
else
out.print(" " + inputArray2D[i][j] + " ");
}
out.println();
}
}
public static int[][] convolution2D(int[][] inputArray2D, int width,
int height, int[][] kernel, int kernelWidth, int kernelHeight) {
assert (inputArray2D.length == inputArray2D[0].length || inputArray2D == null) : "BadInputArrayException"
+ (inputArray2D.length - inputArray2D[0].length == 0);
assert (kernel.length == kernel[0].length || kernel != null) : "BadInputArrayException"
+ (kernel.length - kernel[0].length == 0);
int smallWidth = width - kernelWidth + 1;
int smallHeight = height - kernelHeight + 1;
int[][] output = new int[smallWidth][smallHeight];
for (int i = 0; i < smallWidth; ++i)
for (int j = 0; j < smallHeight; ++j)
output[i][j] = singlePixelConvolution(inputArray2D, i, j,
kernel, kernelWidth, kernelHeight);
return output;
}
public static int[][] g(int[][] inputArray2DA, int[][] inputArray2DB) {
int[][] output = new int[inputArray2DA.length][inputArray2DA.length];
for (int i = 0; i < inputArray2DA.length; ++i)
for (int j = 0; j < inputArray2DA.length; ++j)
output[i][j] = abs(inputArray2DA[i][j])
+ abs(inputArray2DB[i][j]);
return output;
}
public static int[] convolutionDoublePadded(int[][] inputArray2D,
int width, int height, int[][] kernel, int kernelWidth,
int kernelHeight) {
int[][] result2D = new int[width][height];
result2D = convolution2DPadded(inputArray2D, width, height, kernel,
kernelWidth, kernelHeight);
int[] result = new int[width * height];
for (int j = 0; j < height; ++j) {
for (int i = 0; i < width; ++i) {
result[j * width + i] = result2D[i][j];
}
}
return result;
}
public static int[][] convolution2DPadded(int[][] inputArray2D, int width,
int height, int[][] kernel, int kernelWidth, int kernelHeight) {
int smallWidth = width - kernelWidth + 1;
int smallHeight = height - kernelHeight + 1;
int top = kernelHeight / 2;
int left = kernelWidth / 2;
int small[][] = new int[smallWidth][smallHeight];
small = convolution2D(inputArray2D, width, height, kernel, kernelWidth,
kernelHeight);
int large[][] = new int[width][height];
for (int j = 0; j < smallHeight; ++j) {
for (int i = 0; i < smallWidth; ++i) {
large[i + left][j + top] = small[i][j];
}
}
return large;
}
public static int singlePixelConvolution(int[][] inputArray2D, int x,
int y, int[][] k, int kernelWidth, int kernelHeight) {
assert (inputArray2D.length == inputArray2D[0].length || inputArray2D == null) : "BadInputArrayException"
+ (inputArray2D.length - inputArray2D[0].length == 0);
int output = 0;
for (int i = 0; i < kernelWidth; ++i)
for (int j = 0; j < kernelHeight; ++j)
output = output + (inputArray2D[x + i][y + j] * k[i][j]);
return output;
}
public static void fill(int[][] inputArray2D) {
assert (inputArray2D.length == inputArray2D[0].length || inputArray2D == null) : "BadInputArrayException"
+ (inputArray2D.length - inputArray2D[0].length == 0);
for (int i = 0; i < inputArray2D.length; i++)
inputArray2D[i][i] = 1;
}
}
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