I was wondering if when you call color.HSBtoRGB if the hue value would be entered as a range of 0-255, 0-1, 0-360? I am inquiring because I am trying to convert an edge angle to a color but it is only giving me blue or purple? can anyone explain what I am doing?
public void sobelGrey(){
this.greyScale();
double edgex;
double edgey;
Picture pi = new Picture(this.getWidth(), this.getHeight());
Picture tou = new Picture(this.getWidth(), this.getHeight());
Pixel[][] Y = pi.getPixels2D();
Pixel[][] X = tou.getPixels2D();
Pixel[][] h = this.getPixels2D();
for (int y = 1; y< X.length-1; y++){
for(int x= 1; x<X[1].length-1; x++){
edgex =
h[y-1][x-1].getRed() * -1 +
h[y][x-1].getRed() * -2+
h[y+1][x-1].getRed() * -1+
h[y-1][x+1].getRed() * 1 +
h[y][x+1].getRed() * 2+
h[y+1][x+1].getRed() * 1;
Y[y][x].setRed((int)Math.abs(edgex/2));
Y[y][x].setGreen((int)Math.abs(edgex/2));
Y[y][x].setBlue((int)Math.abs(edgex/2));
}
}
for (int y = 1; y< X.length-1; y++){
for(int x= 1; x<X[1].length-1; x++){
edgex =
h[y-1][x-1].getRed() * -1 +
h[y-1][x].getRed() * -2+
h[y-1][x+1].getRed() * -1+
h[y+1][x-1].getRed() * 1 +
h[y+1][x].getRed() * 2+
h[y+1][x+1].getRed() * 1;
X[y][x].setRed((int)Math.abs(edgex/2));
X[y][x].setGreen((int)Math.abs(edgex/2));
X[y][x].setBlue((int)Math.abs(edgex/2));
}
}
for (int y = 1; y< X.length-1; y++){
for(int x= 1; x<X[1].length-1; x++){
int x1 = (int) Math.sqrt(Math.pow(X[y][x].getRed(), 2) + Math.pow(X[y][x].getGreen(), 2) + Math.pow(X[y][x].getBlue(), 2));
int y1 = (int) Math.sqrt(Math.pow(Y[y][x].getRed(), 2) + Math.pow(Y[y][x].getGreen(), 2) + Math.pow(Y[y][x].getBlue(), 2));
int hr = (int) (200/(2*Math.PI)*(Math.tanh(y1/ (x1+.000000000000001))));
int rgb = Color.HSBtoRGB(hr/255, hr, (int) Math.sqrt(Math.pow(x1, 2) + Math.pow(y1, 2)));
Color fixed = new Color(rgb&0xFF*7/10, (rgb>>8)&0xFF*80/255/10, (rgb>>16)&0xFF*4/10);
if( !(Math.sqrt(Math.pow(x1, 2) + Math.pow(y1, 2))< 40))
h[y][x].setColor(fixed);
else
h[y][x].setColor(Color.black);
}
}
pi.explore();
tou.explore();
explore();
}
i am using a computer science AP image processing from Eimacs, and using the swan
You declared hr (and the other variables) to be an int. Then in Color.HSBtoRGB(hr/255, ... you divide an int by an int. For all values of hr below 255, the result will be 0.
Probably it is sufficient to divide by 255.0 to force a floating point division.
I am currently making a game in Java and I am trying to draw an image on my screen, but nothing show up ( only a black screen but no errors ) :(
Here is the code to import the image:
public static Bitmap loadBitmap(String fileName) {
try {
BufferedImage img = ImageIO.read(Art.class.getResource(fileName));
int w = img.getWidth();
int h = img.getHeight();
Bitmap result = new Bitmap(w, h);
img.getRGB(0, 0, w, h, result.pixels, 0, w);
for (int I = 0; I < result.pixels.length; i++) {
int in = result.pixels[i];
int col = (in & 0xf) >> 2;
if (in == 0xffff00ff) col = -1;
result.pixels[i] = col;
}
return result;
} catch (Exception e) {
throw new RuntimeException(e);
}
}
And the Bitmap class:
public void draw(Bitmap bitmap, int xOffs, int yOffs)
{
for(int y = 0; y < bitmap.height; y++)
{
int yPix = y + yOffs;
if(yPix < 0 || yPix >= height) continue;
for(int x = 0; x < bitmap.width; x++)
{
int xPix = x + xOffs;
if(xPix < 0 || xPix >= width) continue;
int alpha = bitmap.pixels[x + y * bitmap.width];
if(alpha > 0)
pixels[xPix + yPix * width] = bitmap.pixels[x + y * bitmap.width];
}
}
}
And to draw all of this :
public void render(Game game)
{
for(int y = 0; y < height; y++)
{
float yd = ((y + 0.5f) - height / 2.0f) / height;
if(yd < 0) yd *= -1;
float z = 10 / yd;
for(int x = 0; x < width; x++)
{
float xd = (x - width / 2.0f) / height;
xd *= z;
int xx = (int) (xd) & 7;
int yy = (int) (z + game.time * 0.1f) & 7;
pixels[x + y * width] = Art.floors.pixels[xx + yy * 64];
}
}
}
I have no errors! I don't really understand.. is this a bug caused by alpha or something? Ho and my image.png is 64x64 made in paint.net
After solving my previous error, here comes another NPE for this code:
public void displayCircles() {
for (Circle menu : circles) {
if (menu.getX() < defaultCircles[pulseCircle].getX() + 0.5 &&
menu.getX() > defaultCircles[pulseCircle].getX() - 0.5 &&
menu.getY() < defaultCircles[pulseCircle].getY() + 0.5 &&
menu.getY() > defaultCircles[pulseCircle].getY() - 0.5)
{
choiceCircle = menu;
pulsingEffect(menu);
}
The third line of the above mentioned codes are giving me NPE. Any ideas?
defaultCircles:
Circle[] defaultCircles = new Circle[circles.length];
void polygon(int n, float cx, float cy, float r) {
float angle = 210.0f / n;
for (int i = 0; i < n; i++) {
circles[i] = new Circle(i + 1, cx + r * cos(radians(angle * i)), cy + r * sin(radians(angle * i)));
defaultCircles[i] = new Circle(i + 1, cx + r * cos(radians(angle * i)), cy + r * sin(radians(angle * i)));
}
}
pulseCircle:
void findPulseCircle() {
float max = circles[0].getY();
for (int i = 0; i < circles.length; i++) {
if (circles[i].getY() > max) {
max = circles[i].getY();
pulseCircle = i;
}
}
}
I have coded a heightmap but it seems to lag the client. I just don't know how to increase the fps. I get about 3-6fps with the heightmap. Im using a quite large bmp for the heightmap, I think its 1024x1024. When i use a smaller on its fine, maybe im just not using the code effectively. Is there a better way to code this heightmap or did I just code it wrong. It is my first time I have worked on a heightmap. Thanks
public class HeightMap {
private final float xScale, yScale, zScale;
private float[][] heightMap;
private FloatBuffer vertices, normals, texCoords;
private IntBuffer indices;
private Vector3f[] verticesArray, normalsArray;
private int[] indicesArray;
private int width;
private int height;
public float getHeight(int x, int y) {
return heightMap[x][y] * yScale;
}
public HeightMap(String path, int resolution) {
heightMap = loadHeightmap("heightmap.bmp");
xScale = 1000f / resolution;
yScale = 8;
zScale = 1000f / resolution;
verticesArray = new Vector3f[width * height];
vertices = BufferUtils.createFloatBuffer(3 * width * height);
texCoords = BufferUtils.createFloatBuffer(2 * width * height);
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
final int pos = height * x + y;
final Vector3f vertex = new Vector3f(xScale * x, yScale * heightMap[x][y], zScale * y);
verticesArray[pos] = vertex;
vertex.store(vertices);
texCoords.put(x / (float) width);
texCoords.put(y / (float) height);
}
}
vertices.flip();
texCoords.flip();
normalsArray = new Vector3f[height * width];
normals = BufferUtils.createFloatBuffer(3 * width * height);
final float xzScale = xScale;
for (int x = 0; x < width; ++x) {
for (int y = 0; y < height; ++y) {
final int nextX = x < width - 1 ? x + 1 : x;
final int prevX = x > 0 ? x - 1 : x;
float sx = heightMap[nextX][y] - heightMap[prevX][y];
if (x == 0 || x == width - 1) {
sx *= 2;
}
final int nextY = y < height - 1 ? y + 1 : y;
final int prevY = y > 0 ? y - 1 : y;
float sy = heightMap[x][nextY] - heightMap[x][prevY];
if (y == 0 || y == height - 1) {
sy *= 2;
}
final Vector3f normal = new Vector3f(-sx * yScale, 2 * xzScale, sy * yScale).normalise(null);
normalsArray[height * x + y] = normal;
normal.store(normals);
}
}
normals.flip();
indicesArray = new int[6 * (height - 1) * (width - 1)];
indices = BufferUtils.createIntBuffer(6 * (width - 1) * (height - 1));
for (int i = 0; i < width - 1; i++) {
for (int j = 0; j < height - 1; j++) {
int pos = (height - 1) * i + j;
indices.put(height * i + j);
indices.put(height * (i + 1) + j);
indices.put(height * (i + 1) + (j + 1));
indicesArray[6 * pos] = height * i + j;
indicesArray[6 * pos + 1] = height * (i + 1) + j;
indicesArray[6 * pos + 2] = height * (i + 1) + (j + 1);
indices.put(height * i + j);
indices.put(height * i + (j + 1));
indices.put(height * (i + 1) + (j + 1));
indicesArray[6 * pos + 3] = height * i + j;
indicesArray[6 * pos + 4] = height * i + (j + 1);
indicesArray[6 * pos + 5] = height * (i + 1) + (j + 1);
}
}
indices.flip();
}
private float[][] loadHeightmap(String fileName) {
try {
BufferedImage img = ImageIO.read(ResourceLoader.getResourceAsStream(fileName));
width = img.getWidth();
height = img.getHeight();
float[][] heightMap = new float[width][height];
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
heightMap[x][y] = 0xFF & img.getRGB(x, y);
}
}
return heightMap;
} catch (IOException e) {
System.out.println("Nincs meg a heightmap!");
return null;
}
}
public void render() {
glEnableClientState(GL_NORMAL_ARRAY);
glEnableClientState(GL_VERTEX_ARRAY);
glEnableClientState(GL_TEXTURE_COORD_ARRAY);
glNormalPointer(0, normals);
glVertexPointer(3, 0, vertices);
glTexCoordPointer(2, 0, texCoords);
glDrawElements(GL_TRIANGLE_STRIP, indices);
glDisableClientState(GL_NORMAL_ARRAY);
glDisableClientState(GL_TEXTURE_COORD_ARRAY);
glDisableClientState(GL_VERTEX_ARRAY);
}
}
Sorry to bring up an old topic, however i see a lot of people ask this:
Use a display list, instead of re-making the heightmap every time.
TheCodingUniverse has a good tutorial on how to do this.
I am looking for a copy paste implementation of Canny Edge Detection in the processing language. I have zero idea about Image processing and very little clue about Processing, though I understand java pretty well.
Can some processing expert tell me if there is a way of implementing this http://www.tomgibara.com/computer-vision/CannyEdgeDetector.java in processing?
I think if you treat processing in lights of Java then some of the problems could be solved very easily. What it means is that you can use Java classes as such.
For the demo I am using the implementation which you have shared.
>>Original Image
>>Changed Image
>>Code
import java.awt.image.BufferedImage;
import java.util.Arrays;
PImage orig;
PImage changed;
void setup() {
orig = loadImage("c:/temp/image.png");
size(250, 166);
CannyEdgeDetector detector = new CannyEdgeDetector();
detector.setLowThreshold(0.5f);
detector.setHighThreshold(1f);
detector.setSourceImage((java.awt.image.BufferedImage)orig.getImage());
detector.process();
BufferedImage edges = detector.getEdgesImage();
changed = new PImage(edges);
noLoop();
}
void draw()
{
//image(orig, 0,0, width, height);
image(changed, 0,0, width, height);
}
// The code below is taken from "http://www.tomgibara.com/computer-vision/CannyEdgeDetector.java"
// I have stripped the comments for conciseness
public class CannyEdgeDetector {
// statics
private final static float GAUSSIAN_CUT_OFF = 0.005f;
private final static float MAGNITUDE_SCALE = 100F;
private final static float MAGNITUDE_LIMIT = 1000F;
private final static int MAGNITUDE_MAX = (int) (MAGNITUDE_SCALE * MAGNITUDE_LIMIT);
// fields
private int height;
private int width;
private int picsize;
private int[] data;
private int[] magnitude;
private BufferedImage sourceImage;
private BufferedImage edgesImage;
private float gaussianKernelRadius;
private float lowThreshold;
private float highThreshold;
private int gaussianKernelWidth;
private boolean contrastNormalized;
private float[] xConv;
private float[] yConv;
private float[] xGradient;
private float[] yGradient;
// constructors
/**
* Constructs a new detector with default parameters.
*/
public CannyEdgeDetector() {
lowThreshold = 2.5f;
highThreshold = 7.5f;
gaussianKernelRadius = 2f;
gaussianKernelWidth = 16;
contrastNormalized = false;
}
public BufferedImage getSourceImage() {
return sourceImage;
}
public void setSourceImage(BufferedImage image) {
sourceImage = image;
}
public BufferedImage getEdgesImage() {
return edgesImage;
}
public void setEdgesImage(BufferedImage edgesImage) {
this.edgesImage = edgesImage;
}
public float getLowThreshold() {
return lowThreshold;
}
public void setLowThreshold(float threshold) {
if (threshold < 0) throw new IllegalArgumentException();
lowThreshold = threshold;
}
public float getHighThreshold() {
return highThreshold;
}
public void setHighThreshold(float threshold) {
if (threshold < 0) throw new IllegalArgumentException();
highThreshold = threshold;
}
public int getGaussianKernelWidth() {
return gaussianKernelWidth;
}
public void setGaussianKernelWidth(int gaussianKernelWidth) {
if (gaussianKernelWidth < 2) throw new IllegalArgumentException();
this.gaussianKernelWidth = gaussianKernelWidth;
}
public float getGaussianKernelRadius() {
return gaussianKernelRadius;
}
public void setGaussianKernelRadius(float gaussianKernelRadius) {
if (gaussianKernelRadius < 0.1f) throw new IllegalArgumentException();
this.gaussianKernelRadius = gaussianKernelRadius;
}
public boolean isContrastNormalized() {
return contrastNormalized;
}
public void setContrastNormalized(boolean contrastNormalized) {
this.contrastNormalized = contrastNormalized;
}
// methods
public void process() {
width = sourceImage.getWidth();
height = sourceImage.getHeight();
picsize = width * height;
initArrays();
readLuminance();
if (contrastNormalized) normalizeContrast();
computeGradients(gaussianKernelRadius, gaussianKernelWidth);
int low = Math.round(lowThreshold * MAGNITUDE_SCALE);
int high = Math.round( highThreshold * MAGNITUDE_SCALE);
performHysteresis(low, high);
thresholdEdges();
writeEdges(data);
}
// private utility methods
private void initArrays() {
if (data == null || picsize != data.length) {
data = new int[picsize];
magnitude = new int[picsize];
xConv = new float[picsize];
yConv = new float[picsize];
xGradient = new float[picsize];
yGradient = new float[picsize];
}
}
private void computeGradients(float kernelRadius, int kernelWidth) {
//generate the gaussian convolution masks
float kernel[] = new float[kernelWidth];
float diffKernel[] = new float[kernelWidth];
int kwidth;
for (kwidth = 0; kwidth < kernelWidth; kwidth++) {
float g1 = gaussian(kwidth, kernelRadius);
if (g1 <= GAUSSIAN_CUT_OFF && kwidth >= 2) break;
float g2 = gaussian(kwidth - 0.5f, kernelRadius);
float g3 = gaussian(kwidth + 0.5f, kernelRadius);
kernel[kwidth] = (g1 + g2 + g3) / 3f / (2f * (float) Math.PI * kernelRadius * kernelRadius);
diffKernel[kwidth] = g3 - g2;
}
int initX = kwidth - 1;
int maxX = width - (kwidth - 1);
int initY = width * (kwidth - 1);
int maxY = width * (height - (kwidth - 1));
//perform convolution in x and y directions
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
int index = x + y;
float sumX = data[index] * kernel[0];
float sumY = sumX;
int xOffset = 1;
int yOffset = width;
for(; xOffset < kwidth ;) {
sumY += kernel[xOffset] * (data[index - yOffset] + data[index + yOffset]);
sumX += kernel[xOffset] * (data[index - xOffset] + data[index + xOffset]);
yOffset += width;
xOffset++;
}
yConv[index] = sumY;
xConv[index] = sumX;
}
}
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
float sum = 0f;
int index = x + y;
for (int i = 1; i < kwidth; i++)
sum += diffKernel[i] * (yConv[index - i] - yConv[index + i]);
xGradient[index] = sum;
}
}
for (int x = kwidth; x < width - kwidth; x++) {
for (int y = initY; y < maxY; y += width) {
float sum = 0.0f;
int index = x + y;
int yOffset = width;
for (int i = 1; i < kwidth; i++) {
sum += diffKernel[i] * (xConv[index - yOffset] - xConv[index + yOffset]);
yOffset += width;
}
yGradient[index] = sum;
}
}
initX = kwidth;
maxX = width - kwidth;
initY = width * kwidth;
maxY = width * (height - kwidth);
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
int index = x + y;
int indexN = index - width;
int indexS = index + width;
int indexW = index - 1;
int indexE = index + 1;
int indexNW = indexN - 1;
int indexNE = indexN + 1;
int indexSW = indexS - 1;
int indexSE = indexS + 1;
float xGrad = xGradient[index];
float yGrad = yGradient[index];
float gradMag = hypot(xGrad, yGrad);
//perform non-maximal supression
float nMag = hypot(xGradient[indexN], yGradient[indexN]);
float sMag = hypot(xGradient[indexS], yGradient[indexS]);
float wMag = hypot(xGradient[indexW], yGradient[indexW]);
float eMag = hypot(xGradient[indexE], yGradient[indexE]);
float neMag = hypot(xGradient[indexNE], yGradient[indexNE]);
float seMag = hypot(xGradient[indexSE], yGradient[indexSE]);
float swMag = hypot(xGradient[indexSW], yGradient[indexSW]);
float nwMag = hypot(xGradient[indexNW], yGradient[indexNW]);
float tmp;
if (xGrad * yGrad <= (float) 0 /*(1)*/
? Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * neMag - (xGrad + yGrad) * eMag) /*(3)*/
&& tmp > Math.abs(yGrad * swMag - (xGrad + yGrad) * wMag) /*(4)*/
: (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * neMag - (yGrad + xGrad) * nMag) /*(3)*/
&& tmp > Math.abs(xGrad * swMag - (yGrad + xGrad) * sMag) /*(4)*/
: Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * seMag + (xGrad - yGrad) * eMag) /*(3)*/
&& tmp > Math.abs(yGrad * nwMag + (xGrad - yGrad) * wMag) /*(4)*/
: (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * seMag + (yGrad - xGrad) * sMag) /*(3)*/
&& tmp > Math.abs(xGrad * nwMag + (yGrad - xGrad) * nMag) /*(4)*/
) {
magnitude[index] = gradMag >= MAGNITUDE_LIMIT ? MAGNITUDE_MAX : (int) (MAGNITUDE_SCALE * gradMag);
//NOTE: The orientation of the edge is not employed by this
//implementation. It is a simple matter to compute it at
//this point as: Math.atan2(yGrad, xGrad);
} else {
magnitude[index] = 0;
}
}
}
}
private float hypot(float x, float y) {
return (float) Math.hypot(x, y);
}
private float gaussian(float x, float sigma) {
return (float) Math.exp(-(x * x) / (2f * sigma * sigma));
}
private void performHysteresis(int low, int high) {
Arrays.fill(data, 0);
int offset = 0;
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
if (data[offset] == 0 && magnitude[offset] >= high) {
follow(x, y, offset, low);
}
offset++;
}
}
}
private void follow(int x1, int y1, int i1, int threshold) {
int x0 = x1 == 0 ? x1 : x1 - 1;
int x2 = x1 == width - 1 ? x1 : x1 + 1;
int y0 = y1 == 0 ? y1 : y1 - 1;
int y2 = y1 == height -1 ? y1 : y1 + 1;
data[i1] = magnitude[i1];
for (int x = x0; x <= x2; x++) {
for (int y = y0; y <= y2; y++) {
int i2 = x + y * width;
if ((y != y1 || x != x1)
&& data[i2] == 0
&& magnitude[i2] >= threshold) {
follow(x, y, i2, threshold);
return;
}
}
}
}
private void thresholdEdges() {
for (int i = 0; i < picsize; i++) {
data[i] = data[i] > 0 ? -1 : 0xff000000;
}
}
private int luminance(float r, float g, float b) {
return Math.round(0.299f * r + 0.587f * g + 0.114f * b);
}
private void readLuminance() {
int type = sourceImage.getType();
if (type == BufferedImage.TYPE_INT_RGB || type == BufferedImage.TYPE_INT_ARGB) {
int[] pixels = (int[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
for (int i = 0; i < picsize; i++) {
int p = pixels[i];
int r = (p & 0xff0000) >> 16;
int g = (p & 0xff00) >> 8;
int b = p & 0xff;
data[i] = luminance(r, g, b);
}
} else if (type == BufferedImage.TYPE_BYTE_GRAY) {
byte[] pixels = (byte[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
for (int i = 0; i < picsize; i++) {
data[i] = (pixels[i] & 0xff);
}
} else if (type == BufferedImage.TYPE_USHORT_GRAY) {
short[] pixels = (short[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
for (int i = 0; i < picsize; i++) {
data[i] = (pixels[i] & 0xffff) / 256;
}
} else if (type == BufferedImage.TYPE_3BYTE_BGR) {
byte[] pixels = (byte[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
int offset = 0;
for (int i = 0; i < picsize; i++) {
int b = pixels[offset++] & 0xff;
int g = pixels[offset++] & 0xff;
int r = pixels[offset++] & 0xff;
data[i] = luminance(r, g, b);
}
} else {
throw new IllegalArgumentException("Unsupported image type: " + type);
}
}
private void normalizeContrast() {
int[] histogram = new int[256];
for (int i = 0; i < data.length; i++) {
histogram[data[i]]++;
}
int[] remap = new int[256];
int sum = 0;
int j = 0;
for (int i = 0; i < histogram.length; i++) {
sum += histogram[i];
int target = sum*255/picsize;
for (int k = j+1; k <=target; k++) {
remap[k] = i;
}
j = target;
}
for (int i = 0; i < data.length; i++) {
data[i] = remap[data[i]];
}
}
private void writeEdges(int pixels[]) {
if (edgesImage == null) {
edgesImage = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
}
edgesImage.getWritableTile(0, 0).setDataElements(0, 0, width, height, pixels);
}
}
I've been spending some time with the Gibara Canny implementation and I'm inclined to agree with Settembrini's comment above; further to this one needs to change the implementation of the Gaussian Kernel generation.
The Gibara Canny uses:
(g1 + g2 + g3) / 3f / (2f * (float) Math.PI * kernelRadius * kernelRadius)
The averaging across a pixel (+-0.5 pixels) in (g1 + g2 + g3) / 3f is great, but the correct variance calculation on the bottom half of the equation for single dimensions is:
(g1 + g2 + g3) / 3f / (Math.sqrt(2f * (float) Math.PI) * kernelRadius)
The standard deviation kernelRadius is sigma in the following equation:
Single direction gaussian
I'm assuming that Gibara is attempting to implement the two dimensional gaussian from the following equation: Two dimensional gaussian where the convolution is a direct product of each gaussian. Whilst this is probably possible and more concise, the following code will correctly convolve in two directions with the above variance calculation:
// First Convolution
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += sourceImage.width) {
int index = x + y;
float sumX = data[index] * kernel[0];
int xOffset = 1;
int yOffset = sourceImage.width;
for(; xOffset < k ;) {;
sumX += kernel[xOffset] * (data[index - xOffset] + data[index + xOffset]);
yOffset += sourceImage.width;
xOffset++;
}
xConv[index] = sumX;
}
}
// Second Convolution
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += sourceImage.width) {
int index = x + y;
float sumY = xConv[index] * kernel[0];
int xOffset = 1;
int yOffset = sourceImage.width;
for(; xOffset < k ;) {;
sumY += xConv[xOffset] * (xConv[index - xOffset] + xConv[index + xOffset]);
yOffset += sourceImage.width;
xOffset++;
}
yConv[index] = sumY;
}
}
NB the yConv[] is now the bidirectional convolution, so the following gradient Sobel calculations are as follows:
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += sourceImage.width) {
float sum = 0f;
int index = x + y;
for (int i = 1; i < k; i++)
sum += diffKernel[i] * (yConv[index - i] - yConv[index + i]);
xGradient[index] = sum;
}
}
for (int x = k; x < sourceImage.width - k; x++) {
for (int y = initY; y < maxY; y += sourceImage.width) {
float sum = 0.0f;
int index = x + y;
int yOffset = sourceImage.width;
for (int i = 1; i < k; i++) {
sum += diffKernel[i] * (yConv[index - yOffset] - yConv[index + yOffset]);
yOffset += sourceImage.width;
}
yGradient[index] = sum;
}
}
Gibara's very neat implementation of non-maximum suppression requires that these gradients be calculated seperately, however if you want to output an image with these gradients one can sum them using either Euclidean or Manhattan distances, the Euclidean would look like so:
// Calculate the Euclidean distance between x & y gradients prior to suppression
int [] gradients = new int [picsize];
for (int i = 0; i < xGradient.length; i++) {
gradients[i] = Math.sqrt(Math.sq(xGradient[i]) + Math.sq(yGradient[i]));
}
Hope this helps, is all in order and apologies for my code! Critique most welcome
In addition to Favonius' answer, you might want to try Greg's OpenCV Processing library which you can now easily install via Sketch > Import Library... > Add Library... and select OpenCV for Processing
After you install the library, you can have a play with the FindEdges example:
import gab.opencv.*;
OpenCV opencv;
PImage src, canny, scharr, sobel;
void setup() {
src = loadImage("test.jpg");
size(src.width, src.height);
opencv = new OpenCV(this, src);
opencv.findCannyEdges(20,75);
canny = opencv.getSnapshot();
opencv.loadImage(src);
opencv.findScharrEdges(OpenCV.HORIZONTAL);
scharr = opencv.getSnapshot();
opencv.loadImage(src);
opencv.findSobelEdges(1,0);
sobel = opencv.getSnapshot();
}
void draw() {
pushMatrix();
scale(0.5);
image(src, 0, 0);
image(canny, src.width, 0);
image(scharr, 0, src.height);
image(sobel, src.width, src.height);
popMatrix();
text("Source", 10, 25);
text("Canny", src.width/2 + 10, 25);
text("Scharr", 10, src.height/2 + 25);
text("Sobel", src.width/2 + 10, src.height/2 + 25);
}
Just as I side note. I studied the Gibara Canny implementation some time ago and found some flaws. E.g. he separates the Gauss-Filtering in 1d filters in x and y direction (which is ok and efficient as such), but then he doesn't apply two passes of those filters (one after another) but just applies SobelX to the x-first-pass-Gauss and SobelY to the y-first-pass-Gauss, which of course leads to low quality gradients. Thus be careful just by copy-past such code.