Canny edge detector: Difference between revisions
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{{task|Image processing}}
;Task:
Write a program that performs so-called [[wp:Canny edge detector|canny edge detection]] on an image.
A possible algorithm consists of the following steps:
# '''Noise reduction.''' May be performed by [[wp:Gaussian blur|Gaussian filter]]. <br>
# Compute '''intensity gradient''' (matrices <math>G_x</math> and <math>G_y</math>) and its '''magnitude''' <math>G</math>:<br> <math>G=\sqrt{G_x^2+G_y^2}</math><br>May be performed by [[image convolution|convolution of an image]] with [[wp:Sobel operator|Sobel operators]]. <br>
# '''Non-maximum suppression.''' <br>For each pixel compute the orientation of intensity gradient vector: <math>\theta = {\rm atan2}\left(G_y, \, G_x\right)</math>. <br>Transform angle <math>\theta</math> to one of four directions: 0, 45, 90, 135 degrees. <br>Compute new array <math>N</math>: if <math>G\left(p_a\right)<G\left(p\right)<G\left(p_b\right)</math><br>where <math>p</math> is the current pixel, <math>p_a</math> and <math>p_b</math> are the two neighbour pixels in the direction of gradient, <br>then <math>N(p) = G(p)</math>, otherwise <math>N(p) = 0</math>. <br>Nonzero pixels in resulting array correspond to local maxima of <math>G</math> in direction <math>\theta(p)</math>. <br>
# '''Tracing edges with hysteresis.''' <br>At this stage two thresholds for the values of <math>G</math> are introduced: <math>T_{min}</math> and <math>T_{max}</math>. <br>Starting from pixels with <math>N(p) \geqslant T_{max}</math>, <br>find all paths of pixels with <math>N(p) \geqslant T_{min}</math> and put them to the resulting image.
<br><br>
=={{header|C}}==
The following program reads an 8 bits per pixel grayscale [[wp:BMP file format|BMP]] file and saves the result to `out.bmp'. Compile with `-lm'.
<
#include <stdio.h>
#include <stdlib.h>
Line 462 ⟶ 467:
free((pixel_t*)out_bitmap_data);
return 0;
}</
=={{header|D}}==
{{trans|C}}
This version retains some of the style of the original C version. This code is faster than the C version, even with the DMD compiler. This version loads and saves PGM images, using the module of the Grayscale image Task.
<
std.algorithm, std.ascii, std.array, bitmap, grayscale_image;
Line 666 ⟶ 670:
printf("Image size: %d x %d\n", imIn.nx, imIn.ny);
imIn.cannyEdgeDetection(45, 50, 1.0f).savePGM("lena_canny.pgm");
}</
=={{header|Go}}==
{{libheader|Imger}}
The example image for this program is the color photograph of a steam engine taken from the Wikipedia article linked to in the task description.
After applying the Canny edge detector, the resulting image is similar to but not quite the same as the Wikipedia image, probably due to differences in the parameters used though a 5×5 Gaussian filter is used in both cases.
Note that on Linux the extension of the example image file name needs to be changed from .PNG to .png in order for the library used to recognize it.
<syntaxhighlight lang="go">package main
import (
ed "github.com/Ernyoke/Imger/edgedetection"
"github.com/Ernyoke/Imger/imgio"
"log"
)
func main() {
img, err := imgio.ImreadRGBA("Valve_original_(1).png")
if err != nil {
log.Fatal("Could not read image", err)
}
cny, err := ed.CannyRGBA(img, 15, 45, 5)
if err != nil {
log.Fatal("Could not perform Canny Edge detection")
}
err = imgio.Imwrite(cny, "Valve_canny_(1).png")
if err != nil {
log.Fatal("Could not write Canny image to disk")
}
}</syntaxhighlight>
=={{header|J}}==
<p>In this solution images are represented as 2D arrays of pixels, with first and second axes representing down and right respectively. Each processing step has a specific pixel representation. In the original and Gaussian-filtered images, array elements represent monochromatic intensity values as numbers ranging from 0 (black) to 255 (white). In the intensity gradient image, gradient values are vectors, and are represented as complex numbers, with real and imaginary components representing down and right respectively. </p>
<p>Detected edge and non-edge points are represented as ones and zeros respectively. An edge is a set of connected edge points (points adjacent horizontally, vertically, or diagonally are considered to be connected). In the final image, each edge is represented by assigning its set of points a common unique value. </p>
<
convolve =: 4 : 'x apply (($x) partition y)'
Line 763 ⟶ 794:
canny =: step5 @ step4 @ step3 @ step2 @ step1
</syntaxhighlight>
<p>The above implementation solves the 'inner problem' of Canny Edge Detection in the J language, with no external dependencies. J's Qt IDE provides additional support including interfaces to image file formats, graphic displays, and the user. The following code exercises these features</p>
<p>The file 'valve.png' referenced in this code is from one of several Wikipedia articles on edge detection. It can be viewed at [https://upload.wikimedia.org/wikipedia/commons/2/2e/Valve_gaussian_%282%29.PNG[https://upload.wikimedia.org/wikipedia/commons/2/2e/Valve_gaussian_%282%29.PNG]]</p>
<syntaxhighlight lang="j">
require 'gl2'
coclass 'edge'
Line 825 ⟶ 856:
form_close=: exit bind 0
run''</
=={{header|Java}}==
El código es de Tom Gibara [The code is from Tom Gibara] (http://www.tomgibara.com/)
Se implementa utilizando una sola clase Java. [It is implemented using a single Java class.]
<syntaxhighlight lang="java">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 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[]) {
//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);
}
}</syntaxhighlight>
=={{header|Julia}}==
{{works with|Julia|0.6}}
<syntaxhighlight lang="julia">using Images
canny_edges = canny(img, sigma = 1.4, upperThreshold = 0.80, lowerThreshold = 0.20)</syntaxhighlight>
=={{header|Mathematica}}/{{header|Wolfram Language}}==
<syntaxhighlight lang="mathematica">Export["out.bmp", EdgeDetect[Import[InputString[]]]];</syntaxhighlight>
Mathematica uses canny edge detection by default. This seems so cheaty next to all of these giant answers...
=={{header|MATLAB}} / {{header|Octave}}==
There is a function in the image processing toolbox [http://www.mathworks.com/help/images/ref/edge.html edge], that has Canny Edge Detection as one of its options.
<syntaxhighlight lang="matlab">BWImage = edge(GrayscaleImage,'canny');</syntaxhighlight>
=={{header|Nim}}==
{{trans|D}}
{{libheader|nimPNG}}
We use the PNG image present on Wikipedia article as input and produce a PNG grayscale image as result.
<syntaxhighlight lang="nim">import lenientops
import math
import nimPNG
const MaxBrightness = 255
type Pixel = int16 # Used instead of byte to be able to store negative values.
#---------------------------------------------------------------------------------------------------
func convolution*[normalize: static bool](input: seq[Pixel]; output: var seq[Pixel];
kernel: seq[float]; nx, ny, kn: int) =
## Do a convolution.
## If normalize is true, map pixels to range 0...maxBrightness.
doAssert kernel.len == kn * kn
doAssert (kn and 1) == 1
doAssert nx > kn and ny > kn
doAssert input.len == output.len
let khalf = kn div 2
when normalize:
var pMin = float.high
var pMax = -float.high
for m in khalf..<(nx - khalf):
for n in khalf..<(ny - khalf):
var pixel = 0.0
var c = 0
for j in -khalf..khalf:
for i in -khalf..khalf:
pixel += input[(n - j) * nx + m - i] * kernel[c]
inc c
if pixel < pMin:
pMin = pixel
if pixel > pMax:
pMax = pixel
for m in khalf..<(nx - khalf):
for n in khalf..<(ny - khalf):
var pixel = 0.0
var c = 0
for j in -khalf..khalf:
for i in -khalf..khalf:
pixel += input[(n - j) * nx + m - i] * kernel[c]
inc c
when normalize:
pixel = MaxBrightness * (pixel - pMin) / (pMax - pMin)
output[n * nx + m] = Pixel(pixel)
#---------------------------------------------------------------------------------------------------
func gaussianFilter(input: seq[Pixel]; output: var seq[Pixel]; nx, ny: int; sigma: float) =
## Apply a gaussian filter.
doAssert input.len == output.len
let n = 2 * (2 * sigma).toInt + 3
let mean = floor(n / 2)
var kernel = newSeq[float](n * n)
var c = 0
for i in 0..<n:
for j in 0..<n:
kernel[c] = exp(-0.5 * (((i - mean) / sigma) ^ 2 + ((j - mean) / sigma) ^ 2)) /
(2 * PI * sigma * sigma)
inc c
convolution[true](input, output, kernel, nx, ny, n)
#---------------------------------------------------------------------------------------------------
proc cannyEdgeDetection(input: seq[Pixel];
nx, ny: int;
tmin, tmax: int;
sigma: float): seq[byte] =
## Detect edges.
var output = newSeq[Pixel](input.len)
gaussianFilter(input, output, nx, ny, sigma)
const Gx = @[float -1, 0, 1,
-2, 0, 2,
-1, 0, 1]
var afterGx = newSeq[Pixel](input.len)
convolution[false](input, afterGx, Gx, nx, ny, 3)
const Gy = @[float 1, 2, 1,
0, 0, 0,
-1, -2, -1]
var afterGy = newSeq[Pixel](input.len)
convolution[false](input, afterGy, Gy, nx, ny, 3)
var g = newSeq[Pixel](input.len)
for i in 1..(nx - 2):
for j in 1..(ny - 2):
let c = i + nx * j
g[c] = hypot(afterGx[c].toFloat, afterGy[c].toFloat).Pixel
# Non-maximum suppression: straightforward implementation.
var nms = newSeq[Pixel](input.len)
for i in 1..(nx - 2):
for j in 1..(ny - 2):
let
c = i + nx * j
nn = c - nx
ss = c + nx
ww = c + 1
ee = c - 1
nw = nn + 1
ne = nn - 1
sw = ss + 1
se = ss - 1
let aux = arctan2(afterGy[c].toFloat, afterGx[c].toFloat) + PI
let dir = aux mod PI / PI * 8
if (((dir <= 1 or dir > 7) and g[c] > g[ee] and g[c] > g[ww]) or # O°.
((dir > 1 and dir <= 3) and g[c] > g[nw] and g[c] > g[se]) or # 45°.
((dir > 3 and dir <= 5) and g[c] > g[nn] and g[c] > g[ss]) or # 90°.
((dir > 5 and dir <= 7) and g[c] > g[ne] and g[c] > g[sw])): # 135°.
nms[c] = g[c]
else:
nms[c] = 0
# Tracing edges with hysteresis. Non-recursive implementation.
var edges = newSeq[int](input.len div 2)
for item in output.mitems: item = 0
var c = 0
for j in 1..(ny - 2):
for i in 1..(nx - 2):
inc c
if nms[c] >= tMax and output[c] == 0:
# Trace edges.
output[c] = MaxBrightness
var nedges = 1
edges[0] = c
while nedges > 0:
dec nedges
let t = edges[nedges]
let neighbors = [t - nx, # nn.
t + nx, # ss.
t + 1, # ww.
t - 1, # ee.
t - nx + 1, # nw.
t - nx - 1, # ne.
t + nx + 1, # sw.
t + nx - 1] # se.
for n in neighbors:
if nms[n] >= tMin and output[n] == 0:
output[n] = MaxBrightness
edges[nedges] = n
inc nedges
# Store the result as a sequence of bytes.
result = newSeqOfCap[byte](output.len)
for val in output:
result.add(byte(val))
#———————————————————————————————————————————————————————————————————————————————————————————————————
when isMainModule:
const
Input = "Valve.png"
Output = "Valve_edges.png"
let pngImage = loadPNG24(seq[byte], Input).get()
# Convert to grayscale and store luminances as 16 bits signed integers.
var pixels = newSeq[Pixel](pngImage.width * pngImage.height)
for i in 0..pixels.high:
pixels[i] = Pixel(0.2126 * pngImage.data[3 * i] +
0.7152 * pngImage.data[3 * i + 1] +
0.0722 * pngImage.data[3 * i + 2] + 0.5)
# Find edges.
let data = cannyEdgeDetection(pixels, pngImage.width, pngImage.height, 45, 50, 1.0)
# Save result as a PNG image.
let status = savePNG(Output, data, LCT_GREY, 8, pngImage.width, pngImage.height)
if status.isOk:
echo "File ", Input, " processed. Result is available in file ", Output
else:
echo "Error: ", status.error</syntaxhighlight>
=={{header|Perl}}==
Used a [https://raw.githubusercontent.com/abend/Image-EdgeDetect/master/lib/Image/EdgeDetect.pm non-CPAN module] by [https://github.com/abend Sasha Kovar]
<syntaxhighlight lang="perl"># 20220120 Perl programming solution
use strict;
use warnings;
use lib '/home/hkdtam/lib';
use Image::EdgeDetect;
my $detector = Image::EdgeDetect->new();
$detector->process('./input.jpg', './output.jpg') or die; # na.cx/i/pHYdUrV.jpg</syntaxhighlight>
Output: [https://drive.google.com/file/d/1ww21DHz-IQTaFKNLC2I2No_fHUehYooG/view (Offsite image file) ]
=={{header|Phix}}==
{{libheader|Phix/pGUI}}
Ported from demo\Arwen32dibdemo\manip.exw (menu entry Manipulate/Filter/Detect Edges, windows-32-bit only) to pGUI.
<!--<syntaxhighlight lang="phix">(notonline)-->
<span style="color: #000080;font-style:italic;">--
-- demo\rosetta\Canny_Edge_Detection.exw
-- =====================================
--</span>
<span style="color: #008080;">without</span> <span style="color: #008080;">js</span> <span style="color: #000080;font-style:italic;">-- imImage, im_width, im_height, im_pixel, IupImageRGB,
-- imFileImageLoadBitmap, and IupImageFromImImage()</span>
<span style="color: #008080;">include</span> <span style="color: #000000;">pGUI</span><span style="color: #0000FF;">.</span><span style="color: #000000;">e</span>
<span style="color: #008080;">constant</span> <span style="color: #000000;">TITLE</span> <span style="color: #0000FF;">=</span> <span style="color: #008000;">"Canny Edge Detection"</span><span style="color: #0000FF;">,</span>
<span style="color: #000000;">IMGFILE</span> <span style="color: #0000FF;">=</span> <span style="color: #008000;">"Valve.png"</span><span style="color: #0000FF;">,</span>
<span style="color: #000000;">C_E_D</span> <span style="color: #0000FF;">=</span> <span style="color: #0000FF;">{{-</span><span style="color: #000000;">1</span><span style="color: #0000FF;">,</span> <span style="color: #0000FF;">-</span><span style="color: #000000;">1</span><span style="color: #0000FF;">,</span> <span style="color: #0000FF;">-</span><span style="color: #000000;">1</span><span style="color: #0000FF;">},</span>
<span style="color: #0000FF;">{-</span><span style="color: #000000;">1</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">8</span><span style="color: #0000FF;">,</span> <span style="color: #0000FF;">-</span><span style="color: #000000;">1</span><span style="color: #0000FF;">},</span>
<span style="color: #0000FF;">{-</span><span style="color: #000000;">1</span><span style="color: #0000FF;">,</span> <span style="color: #0000FF;">-</span><span style="color: #000000;">1</span><span style="color: #0000FF;">,</span> <span style="color: #0000FF;">-</span><span style="color: #000000;">1</span><span style="color: #0000FF;">}}</span>
<span style="color: #008080;">function</span> <span style="color: #000000;">detect_edges</span><span style="color: #0000FF;">(</span><span style="color: #000000;">imImage</span> <span style="color: #000000;">img</span><span style="color: #0000FF;">)</span>
<span style="color: #004080;">integer</span> <span style="color: #000000;">width</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">im_width</span><span style="color: #0000FF;">(</span><span style="color: #000000;">img</span><span style="color: #0000FF;">),</span>
<span style="color: #000000;">height</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">im_height</span><span style="color: #0000FF;">(</span><span style="color: #000000;">img</span><span style="color: #0000FF;">)</span>
<span style="color: #004080;">sequence</span> <span style="color: #000000;">original</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">repeat</span><span style="color: #0000FF;">(</span><span style="color: #7060A8;">repeat</span><span style="color: #0000FF;">(</span><span style="color: #000000;">0</span><span style="color: #0000FF;">,</span><span style="color: #000000;">width</span><span style="color: #0000FF;">),</span><span style="color: #000000;">height</span><span style="color: #0000FF;">)</span>
<span style="color: #004080;">integer</span> <span style="color: #000000;">fh</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">length</span><span style="color: #0000FF;">(</span><span style="color: #000000;">C_E_D</span><span style="color: #0000FF;">),</span> <span style="color: #000000;">hh</span><span style="color: #0000FF;">=(</span><span style="color: #000000;">fh</span><span style="color: #0000FF;">-</span><span style="color: #000000;">1</span><span style="color: #0000FF;">)/</span><span style="color: #000000;">2</span><span style="color: #0000FF;">,</span>
<span style="color: #000000;">fw</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">length</span><span style="color: #0000FF;">(</span><span style="color: #000000;">C_E_D</span><span style="color: #0000FF;">[</span><span style="color: #000000;">1</span><span style="color: #0000FF;">]),</span> <span style="color: #000000;">hw</span><span style="color: #0000FF;">=(</span><span style="color: #000000;">fw</span><span style="color: #0000FF;">-</span><span style="color: #000000;">1</span><span style="color: #0000FF;">)/</span><span style="color: #000000;">2</span><span style="color: #0000FF;">,</span>
<span style="color: #000000;">divisor</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">max</span><span style="color: #0000FF;">(</span><span style="color: #7060A8;">sum</span><span style="color: #0000FF;">(</span><span style="color: #000000;">C_E_D</span><span style="color: #0000FF;">),</span><span style="color: #000000;">1</span><span style="color: #0000FF;">)</span>
<span style="color: #000080;font-style:italic;">-- read original pixels and make them grey,</span>
<span style="color: #008080;">for</span> <span style="color: #000000;">y</span><span style="color: #0000FF;">=</span><span style="color: #000000;">height</span><span style="color: #0000FF;">-</span><span style="color: #000000;">1</span> <span style="color: #008080;">to</span> <span style="color: #000000;">0</span> <span style="color: #008080;">by</span> <span style="color: #0000FF;">-</span><span style="color: #000000;">1</span> <span style="color: #008080;">do</span>
<span style="color: #008080;">for</span> <span style="color: #000000;">x</span><span style="color: #0000FF;">=</span><span style="color: #000000;">0</span> <span style="color: #008080;">to</span> <span style="color: #000000;">width</span><span style="color: #0000FF;">-</span><span style="color: #000000;">1</span> <span style="color: #008080;">do</span>
<span style="color: #004080;">integer</span> <span style="color: #0000FF;">{</span><span style="color: #000000;">c1</span><span style="color: #0000FF;">,</span><span style="color: #000000;">c2</span><span style="color: #0000FF;">,</span><span style="color: #000000;">c3</span><span style="color: #0000FF;">}</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">im_pixel</span><span style="color: #0000FF;">(</span><span style="color: #000000;">img</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">x</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">y</span><span style="color: #0000FF;">),</span>
<span style="color: #000000;">grey</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">floor</span><span style="color: #0000FF;">((</span><span style="color: #000000;">c1</span><span style="color: #0000FF;">*</span><span style="color: #000000;">114</span><span style="color: #0000FF;">+</span><span style="color: #000000;">c2</span><span style="color: #0000FF;">*</span><span style="color: #000000;">587</span><span style="color: #0000FF;">+</span><span style="color: #000000;">c3</span><span style="color: #0000FF;">*</span><span style="color: #000000;">299</span><span style="color: #0000FF;">)/</span><span style="color: #000000;">1000</span><span style="color: #0000FF;">)</span>
<span style="color: #000000;">original</span><span style="color: #0000FF;">[</span><span style="color: #000000;">height</span><span style="color: #0000FF;">-</span><span style="color: #000000;">y</span><span style="color: #0000FF;">,</span><span style="color: #000000;">x</span><span style="color: #0000FF;">+</span><span style="color: #000000;">1</span><span style="color: #0000FF;">]</span> <span style="color: #0000FF;">=</span> <span style="color: #0000FF;">{</span><span style="color: #000000;">grey</span><span style="color: #0000FF;">,</span><span style="color: #000000;">grey</span><span style="color: #0000FF;">,</span><span style="color: #000000;">grey</span><span style="color: #0000FF;">}</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">for</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">for</span>
<span style="color: #000080;font-style:italic;">-- then apply an edge detection filter</span>
<span style="color: #004080;">sequence</span> <span style="color: #000000;">new_image</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">original</span>
<span style="color: #008080;">for</span> <span style="color: #000000;">y</span><span style="color: #0000FF;">=</span><span style="color: #000000;">hh</span><span style="color: #0000FF;">+</span><span style="color: #000000;">1</span> <span style="color: #008080;">to</span> <span style="color: #000000;">height</span><span style="color: #0000FF;">-</span><span style="color: #000000;">hh</span><span style="color: #0000FF;">-</span><span style="color: #000000;">1</span> <span style="color: #008080;">do</span>
<span style="color: #008080;">for</span> <span style="color: #000000;">x</span><span style="color: #0000FF;">=</span><span style="color: #000000;">hw</span><span style="color: #0000FF;">+</span><span style="color: #000000;">1</span> <span style="color: #008080;">to</span> <span style="color: #000000;">width</span><span style="color: #0000FF;">-</span><span style="color: #000000;">hw</span><span style="color: #0000FF;">-</span><span style="color: #000000;">1</span> <span style="color: #008080;">do</span>
<span style="color: #004080;">sequence</span> <span style="color: #000000;">newrgb</span> <span style="color: #0000FF;">=</span> <span style="color: #0000FF;">{</span><span style="color: #000000;">0</span><span style="color: #0000FF;">,</span><span style="color: #000000;">0</span><span style="color: #0000FF;">,</span><span style="color: #000000;">0</span><span style="color: #0000FF;">}</span>
<span style="color: #008080;">for</span> <span style="color: #000000;">i</span><span style="color: #0000FF;">=-</span><span style="color: #000000;">hh</span> <span style="color: #008080;">to</span> <span style="color: #0000FF;">+</span><span style="color: #000000;">hh</span> <span style="color: #008080;">do</span>
<span style="color: #008080;">for</span> <span style="color: #000000;">j</span><span style="color: #0000FF;">=-</span><span style="color: #000000;">hw</span> <span style="color: #008080;">to</span> <span style="color: #0000FF;">+</span><span style="color: #000000;">hw</span> <span style="color: #008080;">do</span>
<span style="color: #000000;">newrgb</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">sq_add</span><span style="color: #0000FF;">(</span><span style="color: #000000;">newrgb</span><span style="color: #0000FF;">,</span><span style="color: #7060A8;">sq_mul</span><span style="color: #0000FF;">(</span><span style="color: #000000;">C_E_D</span><span style="color: #0000FF;">[</span><span style="color: #000000;">i</span><span style="color: #0000FF;">+</span><span style="color: #000000;">hh</span><span style="color: #0000FF;">+</span><span style="color: #000000;">1</span><span style="color: #0000FF;">,</span><span style="color: #000000;">j</span><span style="color: #0000FF;">+</span><span style="color: #000000;">hw</span><span style="color: #0000FF;">+</span><span style="color: #000000;">1</span><span style="color: #0000FF;">],</span><span style="color: #000000;">original</span><span style="color: #0000FF;">[</span><span style="color: #000000;">y</span><span style="color: #0000FF;">+</span><span style="color: #000000;">i</span><span style="color: #0000FF;">,</span><span style="color: #000000;">x</span><span style="color: #0000FF;">+</span><span style="color: #000000;">j</span><span style="color: #0000FF;">]))</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">for</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">for</span>
<span style="color: #000000;">new_image</span><span style="color: #0000FF;">[</span><span style="color: #000000;">y</span><span style="color: #0000FF;">,</span><span style="color: #000000;">x</span><span style="color: #0000FF;">]</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">sq_max</span><span style="color: #0000FF;">(</span><span style="color: #7060A8;">sq_min</span><span style="color: #0000FF;">(</span><span style="color: #7060A8;">sq_floor_div</span><span style="color: #0000FF;">(</span><span style="color: #000000;">newrgb</span><span style="color: #0000FF;">,</span><span style="color: #000000;">divisor</span><span style="color: #0000FF;">),</span><span style="color: #000000;">255</span><span style="color: #0000FF;">),</span><span style="color: #000000;">0</span><span style="color: #0000FF;">)</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">for</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">for</span>
<span style="color: #000000;">new_image</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">flatten</span><span style="color: #0000FF;">(</span><span style="color: #000000;">new_image</span><span style="color: #0000FF;">)</span> <span style="color: #000080;font-style:italic;">-- (as needed by IupImageRGB)</span>
<span style="color: #004080;">Ihandle</span> <span style="color: #000000;">new_img</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">IupImageRGB</span><span style="color: #0000FF;">(</span><span style="color: #000000;">width</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">height</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">new_image</span><span style="color: #0000FF;">)</span>
<span style="color: #008080;">return</span> <span style="color: #000000;">new_img</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">function</span>
<span style="color: #7060A8;">IupOpen</span><span style="color: #0000FF;">()</span>
<span style="color: #000000;">imImage</span> <span style="color: #000000;">im1</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">imFileImageLoadBitmap</span><span style="color: #0000FF;">(</span><span style="color: #000000;">IMGFILE</span><span style="color: #0000FF;">)</span>
<span style="color: #7060A8;">assert</span><span style="color: #0000FF;">(</span><span style="color: #000000;">im1</span><span style="color: #0000FF;">!=</span><span style="color: #004600;">NULL</span><span style="color: #0000FF;">,</span><span style="color: #008000;">"error opening "</span><span style="color: #0000FF;">&</span><span style="color: #000000;">IMGFILE</span><span style="color: #0000FF;">)</span>
<span style="color: #004080;">Ihandle</span> <span style="color: #000000;">label1</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">IupLabel</span><span style="color: #0000FF;">(),</span>
<span style="color: #000000;">label2</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">IupLabel</span><span style="color: #0000FF;">()</span>
<span style="color: #7060A8;">IupSetAttributeHandle</span><span style="color: #0000FF;">(</span><span style="color: #000000;">label1</span><span style="color: #0000FF;">,</span> <span style="color: #008000;">"IMAGE"</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">IupImageFromImImage</span><span style="color: #0000FF;">(</span><span style="color: #000000;">im1</span><span style="color: #0000FF;">))</span>
<span style="color: #7060A8;">IupSetAttributeHandle</span><span style="color: #0000FF;">(</span><span style="color: #000000;">label2</span><span style="color: #0000FF;">,</span> <span style="color: #008000;">"IMAGE"</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">detect_edges</span><span style="color: #0000FF;">(</span><span style="color: #000000;">im1</span><span style="color: #0000FF;">))</span>
<span style="color: #004080;">Ihandle</span> <span style="color: #000000;">dlg</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">IupDialog</span><span style="color: #0000FF;">(</span><span style="color: #7060A8;">IupHbox</span><span style="color: #0000FF;">({</span><span style="color: #000000;">label1</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">label2</span><span style="color: #0000FF;">}),</span><span style="color: #008000;">`TITLE="%s"`</span><span style="color: #0000FF;">,{</span><span style="color: #000000;">TITLE</span><span style="color: #0000FF;">})</span>
<span style="color: #7060A8;">IupShow</span><span style="color: #0000FF;">(</span><span style="color: #000000;">dlg</span><span style="color: #0000FF;">)</span>
<span style="color: #7060A8;">IupMainLoop</span><span style="color: #0000FF;">()</span>
<span style="color: #7060A8;">IupClose</span><span style="color: #0000FF;">()</span>
<!--</syntaxhighlight>-->
=={{header|PHP}}==
PHP implementation
<syntaxhighlight lang="php">
// input: r,g,b in range 0..255
function RGBtoHSV($r, $g, $b) {
$r = $r/255.; // convert to range 0..1
$g = $g/255.;
$b = $b/255.;
$cols = array("r" => $r, "g" => $g, "b" => $b);
asort($cols, SORT_NUMERIC);
$min = key(array_slice($cols, 1)); // "r", "g" or "b"
$max = key(array_slice($cols, -1)); // "r", "g" or "b"
// hue
if($cols[$min] == $cols[$max]) {
$h = 0;
} else {
if($max == "r") {
$h = 60. * ( 0 + ( ($cols["g"]-$cols["b"]) / ($cols[$max]-$cols[$min]) ) );
} elseif ($max == "g") {
$h = 60. * ( 2 + ( ($cols["b"]-$cols["r"]) / ($cols[$max]-$cols[$min]) ) );
} elseif ($max == "b") {
$h = 60. * ( 4 + ( ($cols["r"]-$cols["g"]) / ($cols[$max]-$cols[$min]) ) );
}
if($h < 0) {
$h += 360;
}
}
// saturation
if($cols[$max] == 0) {
$s = 0;
} else {
$s = ( ($cols[$max]-$cols[$min])/$cols[$max] );
$s = $s * 255;
}
// lightness
$v = $cols[$max];
$v = $v * 255;
return(array($h, $s, $v));
}
$filename = "image.png";
$dimensions = getimagesize($filename);
$w = $dimensions[0]; // width
$h = $dimensions[1]; // height
$im = imagecreatefrompng($filename);
for($hi=0; $hi < $h; $hi++) {
for($wi=0; $wi < $w; $wi++) {
$rgb = imagecolorat($im, $wi, $hi);
$r = ($rgb >> 16) & 0xFF;
$g = ($rgb >> 8) & 0xFF;
$b = $rgb & 0xFF;
$hsv = RGBtoHSV($r, $g, $b);
// compare pixel below with current pixel
$brgb = imagecolorat($im, $wi, $hi+1);
$br = ($brgb >> 16) & 0xFF;
$bg = ($brgb >> 8) & 0xFF;
$bb = $brgb & 0xFF;
$bhsv = RGBtoHSV($br, $bg, $bb);
// if difference in hue > 20, edge is detected
if($hsv[2]-$bhsv[2] > 20) {
imagesetpixel($im, $wi, $hi, imagecolorallocate($im, 255, 0, 0));
}
else {
imagesetpixel($im, $wi, $hi, imagecolorallocate($im, 0, 0, 0));
}
}
}
header('Content-Type: image/jpeg');
imagepng($im);
imagedestroy($im);
</syntaxhighlight>
=={{header|Python}}==
In Python,
<
import numpy as np
from scipy.ndimage.filters import convolve, gaussian_filter
Line 848 ⟶ 1,810:
#Gaussian blur to reduce noise
im2 = gaussian_filter(im,
#Use sobel filters to get horizontal and vertical gradients
Line 924 ⟶ 1,886:
im = imread("test.jpg", mode="L") #Open image, convert to greyscale
finalEdges = CannyEdgeDetector(im)
imshow(finalEdges)</
=={{header|Raku}}==
Admittedly laziness always prevails so an off-the-shelf function from ImageMagick is used instead.
cannyedge.c
<syntaxhighlight lang="c">#include <stdio.h>
#include <string.h>
#include <magick/MagickCore.h>
int CannyEdgeDetector(
const char *infile, const char *outfile,
double radius, double sigma, double lower, double upper ) {
ExceptionInfo *exception;
Image *image, *processed_image, *output;
ImageInfo *input_info;
exception = AcquireExceptionInfo();
input_info = CloneImageInfo((ImageInfo *) NULL);
(void) strcpy(input_info->filename, infile);
image = ReadImage(input_info, exception);
output = NewImageList();
processed_image = CannyEdgeImage(image,radius,sigma,lower,upper,exception);
(void) AppendImageToList(&output, processed_image);
(void) strcpy(output->filename, outfile);
WriteImage(input_info, output);
// after-party clean up
DestroyImage(image);
output=DestroyImageList(output);
input_info=DestroyImageInfo(input_info);
exception=DestroyExceptionInfo(exception);
MagickCoreTerminus();
return 0;
}</syntaxhighlight>
cannyedge.raku
<syntaxhighlight lang="raku" line># 20220103 Raku programming solution
use NativeCall;
sub CannyEdgeDetector(CArray[uint8], CArray[uint8], num64, num64, num64, num64
) returns int32 is native( '/home/hkdtam/LibCannyEdgeDetector.so' ) {*};
CannyEdgeDetector( # imagemagick.org/script/command-line-options.php#canny
CArray[uint8].new( 'input.jpg'.encode.list, 0), # pbs.org/wgbh/nova/next/wp-content/uploads/2013/09/fingerprint-1024x575.jpg
CArray[uint8].new( 'output.jpg'.encode.list, 0),
0e0, 2e0, 0.05e0, 0.05e0
)</syntaxhighlight>
{{out}}
<pre>export PKG_CONFIG_PATH=/usr/lib/pkgconfig
gcc -Wall -fPIC -shared -o LibCannyEdgeDetector.so cannyedge.c `pkg-config --cflags --libs MagickCore`
raku -c cannyedge.raku && ./cannyedge.raku</pre>
Output: [https://drive.google.com/file/d/1k9F53egoFP0Sl4uBR85nai8IwC43BkYZ/view (Offsite image file) ]
=={{header|Tcl}}==
{{libheader|crimp}}
<
package require crimp::pgm
Line 944 ⟶ 1,957:
writePGM $outputFile [crimp filter canny sobel [readPGM $inputFile]]
}
cannyFilterFile {*}$argv</
=={{header|Wren}}==
{{trans|C}}
{{libheader|DOME}}
{{libheader|Wren-check}}
<syntaxhighlight lang="wren">import "dome" for Window
import "graphics" for Canvas, Color, ImageData
import "math" for Math
import "./check" for Check
var MaxBrightness = 255
class Canny {
construct new(inFile, outFile) {
Window.title = "Canny edge detection"
var image1 = ImageData.load(inFile)
var w = image1.width
var h = image1.height
Window.resize(w * 2 + 20, h)
Canvas.resize(w * 2 + 20, h)
var image2 = ImageData.create(outFile, w, h)
var pixels = List.filled(w * h, 0)
var ix = 0
// convert image1 to gray scale as a list of pixels
for (y in 0...h) {
for (x in 0...w) {
var c1 = image1.pget(x, y)
var lumin = (0.2126 * c1.r + 0.7152 * c1.g + 0.0722 * c1.b).floor
pixels[ix] = lumin
ix = ix + 1
}
}
// find edges
var data = cannyEdgeDetection(pixels, w, h, 45, 50, 1)
// write to image2
ix = 0
for (y in 0...h) {
for (x in 0...w) {
var d = data[ix]
var c = Color.rgb(d, d, d)
image2.pset(x, y, c)
ix = ix + 1
}
}
// display the two images side by side
image1.draw(0, 0)
image2.draw(w + 20, 0)
// save image2 to outFile
image2.saveToFile(outFile)
}
init() {}
// If normalize is true, map pixels to range 0..MaxBrightness
convolution(input, output, kernel, nx, ny, kn, normalize) {
Check.ok((kn % 2) == 1)
Check.ok(nx > kn && ny > kn)
var khalf = (kn / 2).floor
var min = Num.largest
var max = -min
if (normalize) {
for (m in khalf...nx-khalf) {
for (n in khalf...ny-khalf) {
var pixel = 0
var c = 0
for (j in -khalf..khalf) {
for (i in -khalf..khalf) {
pixel = pixel + input[(n-j)*nx + m - i] * kernel[c]
c = c + 1
}
}
if (pixel < min) min = pixel
if (pixel > max) max = pixel
}
}
}
for (m in khalf...nx-khalf) {
for (n in khalf...ny-khalf) {
var pixel = 0
var c = 0
for (j in -khalf..khalf) {
for (i in -khalf..khalf) {
pixel = pixel + input[(n-j)*nx + m - i] * kernel[c]
c = c + 1
}
}
if (normalize) pixel = MaxBrightness * (pixel - min) / (max - min)
output[n * nx + m] = pixel.truncate
}
}
}
gaussianFilter(input, output, nx, ny, sigma) {
var n = 2 * (2 * sigma).truncate + 3
var mean = (n / 2).floor
var kernel = List.filled(n * n, 0)
System.print("Gaussian filter: kernel size = %(n), sigma = %(sigma)")
var c = 0
for (i in 0...n) {
for (j in 0...n) {
var t = (-0.5 * (((i - mean) / sigma).pow(2) + ((j - mean) / sigma).pow(2))).exp
kernel[c] = t / (2 * Num.pi * sigma * sigma)
c = c + 1
}
}
convolution(input, output, kernel, nx, ny, n, true)
}
// Returns the square root of 'x' squared + 'y' squared.
hypot(x, y) { (x*x + y*y).sqrt }
cannyEdgeDetection(input, nx, ny, tmin, tmax, sigma) {
var output = List.filled(input.count, 0)
gaussianFilter(input, output, nx, ny, sigma)
var Gx = [-1, 0, 1, -2, 0, 2, -1, 0, 1]
var afterGx = List.filled(input.count, 0)
convolution(output, afterGx, Gx, nx, ny, 3, false)
var Gy = [1, 2, 1, 0, 0, 0, -1, -2, -1]
var afterGy = List.filled(input.count, 0)
convolution(output, afterGy, Gy, nx, ny, 3, false)
var G = List.filled(input.count, 0)
for (i in 1..nx-2) {
for (j in 1..ny-2) {
var c = i + nx * j
G[c] = hypot(afterGx[c], afterGy[c]).floor
}
}
// non-maximum suppression: straightforward implementation
var nms = List.filled(input.count, 0)
for (i in 1..nx-2) {
for (j in 1..ny-2) {
var c = i + nx * j
var nn = c - nx
var ss = c + nx
var ww = c + 1
var ee = c - 1
var nw = nn + 1
var ne = nn - 1
var sw = ss + 1
var se = ss - 1
var temp = Math.atan(afterGy[c], afterGx[c]) + Num.pi
var dir = (temp % Num.pi) / Num.pi * 8
if (((dir <= 1 || dir > 7) && G[c] > G[ee] && G[c] > G[ww]) || // O°
((dir > 1 && dir <= 3) && G[c] > G[nw] && G[c] > G[se]) || // 45°
((dir > 3 && dir <= 5) && G[c] > G[nn] && G[c] > G[ss]) || // 90°
((dir > 5 && dir <= 7) && G[c] > G[ne] && G[c] > G[sw])) { // 135°
nms[c] = G[c]
} else {
nms[c] = 0
}
}
}
// tracing edges with hysteresis: non-recursive implementation
var edges = List.filled((input.count/2).floor, 0)
for (i in 0...output.count) output[i] = 0
var c = 1
for (j in 1..ny-2) {
for (i in 1..nx-2) {
if (nms[c] >= tmax && output[c] == 0) {
// trace edges
output[c] = MaxBrightness
var nedges = 1
edges[0] = c
while (true) {
nedges = nedges - 1
var t = edges[nedges]
var nbs = [ // neighbors
t - nx, // nn
t + nx, // ss
t + 1, // ww
t - 1, // ee
t - nx + 1, // nw
t - nx - 1, // ne
t + nx + 1, // sw
t + nx - 1 // se
]
for (n in nbs) {
if (nms[n] >= tmin && output[n] == 0) {
output[n] = MaxBrightness
edges[nedges] = n
nedges = nedges + 1
}
}
if (nedges == 0) break
}
}
c = c + 1
}
}
return output
}
update() {}
draw(alpha) {}
}
var Game = Canny.new("Valve_original.png", "Valve_monchrome_canny.png")</syntaxhighlight>
=={{header|Yabasic}}==
{{trans|Phix}}
<syntaxhighlight lang="yabasic">// Rosetta Code problem: http://rosettacode.org/wiki/Canny_edge_detector
// Adapted from Phix to Yabasic by Galileo, 01/2022
import ReadFromPPM2
MaxBrightness = 255
readPPM("Valve.ppm")
print "Be patient, please ..."
width = peek("winwidth")
height = peek("winheight")
dim pixels(width, height), C_E_D(3, 3)
data -1, -1, -1, -1, 8, -1, -1, -1, -1
for i = 0 to 2
for j = 0 to 2
read C_E_D(i, j)
next
next
// convert image to gray scale
for y = 1 to height
for x = 1 to width
c$ = right$(getbit$(x, y, x, y), 6)
r = dec(left$(c$, 2))
g = dec(mid$(c$, 3, 2))
b = dec(right$(c$, 2))
lumin = floor(0.2126 * r + 0.7152 * g + 0.0722 * b)
pixels(x, y) = lumin
next
next
dim new_image(width, height)
divisor = 1
// apply an edge detection filter
for y = 2 to height-2
for x = 2 to width-2
newrgb = 0
for i = -1 to 1
for j = -1 to 1
newrgb = newrgb + C_E_D(i+1, j+1) * pixels(x+i, y+j)
next
new_image(x, y) = max(min(newrgb / divisor,255),0)
next
next
next
// show result
for x = 1 to width
for y = 1 to height
c = new_image(x, y)
color c, c, c
dot x, y
next
next</syntaxhighlight>
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