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/**
* @author Pacien TRAN-GIRARD
* @author Timothée FLOURE
*/
public final class Filter {
public static final float[][] SMOOTH_CORE = new float[][]{
{0.1f, 0.1f, 0.1f},
{0.1f, 0.2f, 0.1f},
{0.1f, 0.1f, 0.1f}
};
public static final float[][] SOBEL_X_CORE = new float[][]{
{-1, 0, 1},
{-2, 0, 2},
{-1, 0, 1}
};
public static final float[][] SOBEL_Y_CORE = new float[][]{
{-1, -2, -1},
{0, 0, 0},
{1, 2, 1}
};
/**
* Get a pixel without accessing out of bounds
*
* @param gray a HxW float array, H > 0, W > 0
* @param row Y coordinate
* @param col X coordinate
* @return nearest valid pixel color
*/
public static float at(float[][] gray, int row, int col) {
int maxRow = gray.length - 1;
int maxCol = gray[0].length - 1;
if (row < 0) row = 0;
if (col < 0) col = 0;
if (row > maxRow) row = maxRow;
if (col > maxCol) col = maxCol;
return gray[row][col];
}
/**
* Combine neighbour pixels using the given kernel.
*
* @param gray a HxW float array
* @param kernel a MxN float array, with M and N odd
* @param row Y coordinate
* @param col X coordinate
* @return the pixel value
*/
private static float combineNeighbours(float[][] gray, float[][] kernel, int row, int col) {
int kernelWidth = kernel[0].length;
int kernelHeight = kernel.length;
int rowOffset = row - kernelWidth / 2; // int division
int colOffset = col - kernelHeight / 2; // int division
float pixelValue = 0;
for (int kernelRow = 0; kernelRow < kernelHeight; ++kernelRow)
for (int kernelCol = 0; kernelCol < kernelWidth; ++kernelCol)
pixelValue += kernel[kernelRow][kernelCol]
* Filter.at(gray, rowOffset + kernelRow, colOffset + kernelCol);
return pixelValue;
}
/**
* Convolve a single-channel image with specified kernel.
*
* @param gray a HxW float array
* @param kernel a MxN float array, with M and N odd
* @return a HxW float array
*/
public static float[][] filter(float[][] gray, float[][] kernel) {
int width = gray[0].length;
int height = gray.length;
float[][] filteredImage = new float[height][width];
for (int row = 0; row < height; ++row)
for (int col = 0; col < width; ++col)
filteredImage[row][col] = Filter.combineNeighbours(gray, kernel, row, col);
return filteredImage;
}
/**
* Smooth a single-channel image
*
* @param gray a HxW float array
* @return a HxW float array
*/
public static float[][] smooth(float[][] gray) {
return Filter.filter(gray, SMOOTH_CORE);
}
/**
* Compute horizontal Sobel filter
*
* @param gray a HxW float array
* @return a HxW float array
*/
public static float[][] sobelX(float[][] gray) {
return Filter.filter(gray, SOBEL_X_CORE);
}
/**
* Compute vertical Sobel filter
*
* @param gray a HxW float array
* @return a HxW float array
*/
public static float[][] sobelY(float[][] gray) {
return Filter.filter(gray, SOBEL_Y_CORE);
}
/**
* Compute the magnitude of combined Sobel filters
*
* @param gray a HxW float array
* @return a HxW float array
*/
public static float[][] sobel(float[][] gray) {
float[][] x = Filter.sobelX(gray);
float[][] y = Filter.sobelY(gray);
int width = gray[0].length;
int height = gray.length;
float[][] sobelImage = new float[height][width];
for (int row = 0; row < height; ++row) {
for (int col = 0; col < width; ++col) {
sobelImage[row][col] = (float) Math.sqrt(Math.pow(x[row][col], 2) + Math.pow(y[row][col], 2));
}
}
return sobelImage;
}
}
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