Edge detection, sesuai dengan namanya, digunakan untuk mendeteksi tepi dari citra. Tujuan mendeteksi tepi sendiri adalah untuk mengelompokkan objek2 dalam citra, dan juga digunakan untuk menganalisis citra lebih lanjut. Ada banyak algoritma yang digunakan untuk mendeteksi tepi (sobel, prewitt, robert cross, canny, dll), salah satu diantaranya adalah deteksi tepi Canny (Canny Edge detection).
The Canny edge detection operator was developed by John F. Canny in 1986 and uses a multi-stage algorithm to detect a wide range of edges in images. Most importantly, Canny also produced a computational theory of edge detection explaining why the technique works. (wikipedia).
Posting berikut ini adalah contoh implementasi dari deteksi canny dengan menggunakan Java (download di sini (ziddu)):
import java.awt.image.BufferedImage;
import java.util.Arrays;
/**
* <p><em>This software has been released into the public domain.
* <strong>Please read the notes in this source file for additional information.
* </strong></em></p>
*
* <p>This class provides a configurable implementation of the Canny edge
* detection algorithm. This classic algorithm has a number of shortcomings,
* but remains an effective tool in many scenarios. <em>This class is designed
* for single threaded use only.</em></p>
*
* <p>Sample usage:</p>
*
* <pre><code>
* //create the detector
* CannyEdgeDetector detector = new CannyEdgeDetector();
* //adjust its parameters as desired
* detector.setLowThreshold(0.5f);
* detector.setHighThreshold(1f);
* //apply it to an image
* detector.setSourceImage(frame);
* detector.process();
* BufferedImage edges = detector.getEdgesImage();
* </code></pre>
*
* <p>For a more complete understanding of this edge detector's parameters
* consult an explanation of the algorithm.</p>
*
* @author Tom Gibara
*
*/
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;
}
// accessors
/**
* The image that provides the luminance data used by this detector to
* generate edges.
*
* @return the source image, or null
*/
public BufferedImage getSourceImage() {
return sourceImage;
}
/**
* Specifies the image that will provide the luminance data in which edges
* will be detected. A source image must be set before the process method
* is called.
*
* @param image a source of luminance data
*/
public void setSourceImage(BufferedImage image) {
sourceImage = image;
}
/**
* Obtains an image containing the edges detected during the last call to
* the process method. The buffered image is an opaque image of type
* BufferedImage.TYPE_INT_ARGB in which edge pixels are white and all other
* pixels are black.
*
* @return an image containing the detected edges, or null if the process
* method has not yet been called.
*/
public BufferedImage getEdgesImage() {
return edgesImage;
}
/**
* Sets the edges image. Calling this method will not change the operation
* of the edge detector in any way. It is intended to provide a means by
* which the memory referenced by the detector object may be reduced.
*
* @param edgesImage expected (though not required) to be null
*/
public void setEdgesImage(BufferedImage edgesImage) {
this.edgesImage = edgesImage;
}
/**
* The low threshold for hysteresis. The default value is 2.5.
*
* @return the low hysteresis threshold
*/
public float getLowThreshold() {
return lowThreshold;
}
/**
* Sets the low threshold for hysteresis. Suitable values for this parameter
* must be determined experimentally for each application. It is nonsensical
* (though not prohibited) for this value to exceed the high threshold value.
*
* @param threshold a low hysteresis threshold
*/
public void setLowThreshold(float threshold) {
if (threshold < 0) throw new IllegalArgumentException();
lowThreshold = threshold;
}
/**
* The high threshold for hysteresis. The default value is 7.5.
*
* @return the high hysteresis threshold
*/
public float getHighThreshold() {
return highThreshold;
}
/**
* Sets the high threshold for hysteresis. Suitable values for this
* parameter must be determined experimentally for each application. It is
* nonsensical (though not prohibited) for this value to be less than the
* low threshold value.
*
* @param threshold a high hysteresis threshold
*/
public void setHighThreshold(float threshold) {
if (threshold < 0) throw new IllegalArgumentException();
highThreshold = threshold;
}
/**
* The number of pixels across which the Gaussian kernel is applied.
* The default value is 16.
*
* @return the radius of the convolution operation in pixels
*/
public int getGaussianKernelWidth() {
return gaussianKernelWidth;
}
/**
* The number of pixels across which the Gaussian kernel is applied.
* This implementation will reduce the radius if the contribution of pixel
* values is deemed negligable, so this is actually a maximum radius.
*
* @param gaussianKernelWidth a radius for the convolution operation in
* pixels, at least 2.
*/
public void setGaussianKernelWidth(int gaussianKernelWidth) {
if (gaussianKernelWidth < 2) throw new IllegalArgumentException();
this.gaussianKernelWidth = gaussianKernelWidth;
}
/**
* The radius of the Gaussian convolution kernel used to smooth the source
* image prior to gradient calculation. The default value is 16.
*
* @return the Gaussian kernel radius in pixels
*/
public float getGaussianKernelRadius() {
return gaussianKernelRadius;
}
/**
* Sets the radius of the Gaussian convolution kernel used to smooth the
* source image prior to gradient calculation.
*
* @return a Gaussian kernel radius in pixels, must exceed 0.1f.
*/
public void setGaussianKernelRadius(float gaussianKernelRadius) {
if (gaussianKernelRadius < 0.1f) throw new IllegalArgumentException();
this.gaussianKernelRadius = gaussianKernelRadius;
}
/**
* Whether the luminance data extracted from the source image is normalized
* by linearizing its histogram prior to edge extraction. The default value
* is false.
*
* @return whether the contrast is normalized
*/
public boolean isContrastNormalized() {
return contrastNormalized;
}
/**
* Sets whether the contrast is normalized
* @param contrastNormalized true if the contrast should be normalized,
* false otherwise
*/
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];
}
}
//NOTE: The elements of the method below (specifically the technique for
//non-maximal suppression and the technique for gradient computation)
//are derived from an implementation posted in the following forum (with the
//clear intent of others using the code):
// http://forum.java.sun.com/thread.jspa?threadID=546211&start=45&tstart=0
//My code effectively mimics the algorithm exhibited above.
//Since I don't know the providence of the code that was posted it is a
//possibility (though I think a very remote one) that this code violates
//someone's intellectual property rights. If this concerns you feel free to
//contact me for an alternative, though less efficient, implementation.
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;
/*
* An explanation of what's happening here, for those who want
* to understand the source: This performs the "non-maximal
* supression" phase of the Canny edge detection in which we
* need to compare the gradient magnitude to that in the
* direction of the gradient; only if the value is a local
* maximum do we consider the point as an edge candidate.
*
* We need to break the comparison into a number of different
* cases depending on the gradient direction so that the
* appropriate values can be used. To avoid computing the
* gradient direction, we use two simple comparisons: first we
* check that the partial derivatives have the same sign (1)
* and then we check which is larger (2). As a consequence, we
* have reduced the problem to one of four identical cases that
* each test the central gradient magnitude against the values at
* two points with 'identical support'; what this means is that
* the geometry required to accurately interpolate the magnitude
* of gradient function at those points has an identical
* geometry (upto right-angled-rotation/reflection).
*
* When comparing the central gradient to the two interpolated
* values, we avoid performing any divisions by multiplying both
* sides of each inequality by the greater of the two partial
* derivatives. The common comparand is stored in a temporary
* variable (3) and reused in the mirror case (4).
*
*/
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;
}
}
}
}
//NOTE: It is quite feasible to replace the implementation of this method
//with one which only loosely approximates the hypot function. I've tested
//simple approximations such as Math.abs(x) + Math.abs(y) and they work fine.
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) {
//NOTE: this implementation reuses the data array to store both
//luminance data from the image, and edge intensity from the processing.
//This is done for memory efficiency, other implementations may wish
//to separate these functions.
Arrays.fill(data, 0);
int offset = 0;
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
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[]) {
//NOTE: There is currently no mechanism for obtaining the edge data
//in any other format other than an INT_ARGB type BufferedImage.
//This may be easily remedied by providing alternative accessors.
if (edgesImage == null) {
edgesImage = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
}
edgesImage.getWritableTile(0, 0).setDataElements(0, 0, width, height, pixels);
}
}
Berikut ini adalah source code yang digunakan untuk mengimplementasikan class CannyEdgeDetector di atas:
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import javax.imageio.ImageIO;
/**
*
* @author windu purnomo
*/
public class Canny {
public static void main(String[] args) throws IOException {
File file= new File("smile.jpg");
int c, r, g, b;
BufferedImage frame = ImageIO.read(file);
CannyEdgeDetector detector = new CannyEdgeDetector();
detector.setLowThreshold(0.5f);
detector.setHighThreshold(1f);
detector.setSourceImage(frame);
detector.process();
BufferedImage edges = detector.getEdgesImage();
ImageIO.write(edges,"PNG",new File("CannyEdge.png"));
}
}
CATATAN:
1. Pastikan, kedua source code di atas berada dalam satu folder
2. Pastikan juga image smile.jpg tersedia (atau gunakan file image yang lain) dan berada dalam satu folder
3. Compile: javac *.java
4. Run: java Canny
LINK Download : Canny Edge Detector.
beuhh,,panjang win.. napa engga dioplod bentuk txt ajah kodenya? ^^
di pojok kanan atas, klo dihover ada view source, nah tinggal copy deh, ato save as saja.
mas wktu aku compile baris 533,535,537,539,541 kok eror??gmn cara makenya y,mohon penjelasan thks
@azis: setelah saya cek, ternyata, karakter ; dan beberapa karakter lainnya di ubah jadi smile sama servernya. Jadi klo km copy program java di atas, beberapa sintax sudah berubah. Terimakasih atas koreksinya.
Berikutnya, silahkan pakai source code dari sini: http://www.ziddu.com/download/11482210/CannyEdgeDetector.java.zip.html
dan cara pakainya, sama seperti yang saya tulis di atas.
Hi Windu! Thanks for sharing your Main code for this CannyEdgeDetector by Tom Gibara! I’m working on it too as my class project for image processing but for spatial application. Though i can’t understand much of ur words here, you presented your codes so clear and simply understandable. THANK YOU!
Hi Genevieve! you are welcome…
yg listing kedua itu gmana ya cara run nya??
trus di
import windu.process.CannyEdgeDetector;
kok error gmn y???
thx
1. “yang listing kedua ….”, maksudnya yang mana ya?
2. error windu.proses…., Sebelumnya ini saya ralat, coba reza download source class CannyEdgeDetector nya dari link yang saya berikan di atas. Jadi jangan copy dari halaman ini, karena ada beberapa yang salah. Nah, setelah download, silahkan reza untuk menulis class mainnya seperti pada halaman blog ini, dengan menghapus baris: import windu.proses.CannyEdgeDetector. Selanjutnya ikuti cara menjalankan program sesuai dengan langkah2 yang saya tulis di atas (menggunakan font warna merah).
mas windu linknya broken, ada lagi link lain?
makasih banyak atas tutorial nya mas purnomo…
btw qu ana tanya,, kalo untuk kalkulasi edge detection nya gimana ya…? maksud ana batas tepi nya kita jadikan fungsi…
misi mas
link http://www.ziddu.com/download/11482210/CannyEdgeDetector.java.zip.html broken tolong banget donk mas lagi bener bener butuh sama program canny yang pake java
makasih sebelum dan sesudahnya
mas windu fungsi coding yang
for(; xOffset < kwidth ) {
sumY += kernel[xOffset] * (data[index - yOffset] + data[index + yOffset]);
sumX += kernel[xOffset] * (data[index - xOffset] + data[index + xOffset]);
yOffset += width;
xOffset++;
buat apa mas….. Mohon pencerahanya !
code nya tidak lengkap mas? Saya kurang mengerti
for(; xOffset < kwidth {
534
535 sumY += kernel[xOffset] * (data[index - yOffset] + data[index + yOffset]);
536
537 sumX += kernel[xOffset] * (data[index - xOffset] + data[index + xOffset]);
yang fungsi for dan ada link http nya mas,
saya minta file yang buat di download dong mas, link diatas broken mas….
terimakasih mas !
mas…… kasih tau penjelasan dari codingnya dunk mas…. hehehe
mas cara untuk treshold bagaimana y di java?hehe
mas upload file nya lagi dong..yang di ziddu udah kedelete tuh..mkasih sebelumnya..
mas linknya rusak..saya butuh aplikasi ini..mohon di share lagi dong..atau kirim ke email saya mas.. rivanipai@gmail.com.. mohon bantuannya yah
Mohon maaf atas beberapa problem yang terjadi. Berikutnya silahkan download dari link berikut, kemudian ikuti instruksi yang saya tulis di blog ini untuk menjalankannya. http://code.google.com/p/windupurnomo/downloads/detail?name=Canny%20Edge%20Detector.rar&can=2&q=
Mkasih ya mas…saya sedang coba nih..saya pake netbeans jd mash ad kesalahan yg ga bisa saya compile..
Sebetulnya g pake netbeans pun bisa kok. Cukup pake comand line. Masuk ke direktori hasil ekstraksi, kemudian:
[1]. Compile dgn perintah: javac *.java
[2]. Running dgn perintah: java Canny
iya mas..saya pgn buat GUInya..jd nanti bisa di browse buat di pilih gambarnya baru di edge detector namun GUInya sudh bisa di buat namun belum kedetect untuk cannynya ini..hehe
mas..saya suadah melakukan edge detector namun kalo mau mengambil nilai rgb dari objek hasil sedge detectornya aja gmana ya mas???jadi backgroundnya gak diambil..cukup citra yang di dalam edge detector..mohon bantuannya mas..
mas windu btw punya source code yg metode robert atau sobel atau prewiit..klo boleh kirim ke email saya dunk..thanks alot..:)
mas punya source code yang menggunakan metode robert atau sobel atau prewitt.kalo boleh kirim ke email saya dunk…thanks alot