Image convolution

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Task
Image convolution
You are encouraged to solve this task according to the task description, using any language you may know.

One class of image digital filters is described by a rectangular matrix of real coefficients called kernel convoluted in a sliding window of image pixels. Usually the kernel is square Kkl, where k, l are in the range -R,-R+1,..,R-1,R. W=2R+1 is the kernel width. The filter determines the new value of a monochromatic image pixel Pij as a convolution of the image pixels in the window centered in i, j and the kernel values:

P_{ij}=\displaystyle\sum_{k=-R}^R \sum_{l=-R}^R P_{i+k\ j+l} K_{k l}

Color images are usually split into the channels which are filtered independently. A color model can be changed as well, i.e. filtration is performed not necessarily in RGB. Common kernels sizes are 3x3 and 5x5. The complexity of filtrating grows quadratically (O(n2)) with the kernel width.

Task: Write a generic convolution 3x3 kernel filter. Optionally show some end user filters that use this generic one.

(You can use, to test the functions below, these input and output solutions.)

Contents

[edit] Ada

First we define floating-point stimulus and color pixels which will be then used for filtration:

type Float_Luminance is new Float;
 
type Float_Pixel is record
R, G, B : Float_Luminance := 0.0;
end record;
 
function "*" (Left : Float_Pixel; Right : Float_Luminance) return Float_Pixel is
pragma Inline ("*");
begin
return (Left.R * Right, Left.G * Right, Left.B * Right);
end "*";
 
function "+" (Left, Right : Float_Pixel) return Float_Pixel is
pragma Inline ("+");
begin
return (Left.R + Right.R, Left.G + Right.G, Left.B + Right.B);
end "+";
 
function To_Luminance (X : Float_Luminance) return Luminance is
pragma Inline (To_Luminance);
begin
if X <= 0.0 then
return 0;
elsif X >= 255.0 then
return 255;
else
return Luminance (X);
end if;
end To_Luminance;
 
function To_Pixel (X : Float_Pixel) return Pixel is
pragma Inline (To_Pixel);
begin
return (To_Luminance (X.R), To_Luminance (X.G), To_Luminance (X.B));
end To_Pixel;

Float_Luminance is an unconstrained equivalent of Luminance. Float_Pixel is one to Pixel. Conversion operations To_Luminance and To_Pixel saturate the corresponding values. The operation + is defined per channels. The operation * is defined as multiplying by a scalar. (I.e. Float_Pixel is a vector space.)

Now we are ready to implement the filter. The operation is performed in memory. The access to the image array is minimized using a slid window. The filter is in fact a triplet of filters handling each image channel independently. It can be used with other color models as well.

type Kernel_3x3 is array (-1..1, -1..1) of Float_Luminance;
 
procedure Filter (Picture : in out Image; K : Kernel_3x3) is
function Get (I, J : Integer) return Float_Pixel is
pragma Inline (Get);
begin
if I in Picture'Range (1) and then J in Picture'Range (2) then
declare
Color : Pixel := Picture (I, J);
begin
return (Float_Luminance (Color.R), Float_Luminance (Color.G), Float_Luminance (Color.B));
end;
else
return (others => 0.0);
end if;
end Get;
W11, W12, W13 : Float_Pixel; -- The image window
W21, W22, W23 : Float_Pixel;
W31, W32, W33 : Float_Pixel;
Above : array (Picture'First (2) - 1..Picture'Last (2) + 1) of Float_Pixel;
This  : Float_Pixel;
begin
for I in Picture'Range (1) loop
W11 := Above (Picture'First (2) - 1); -- The upper row is taken from the cache
W12 := Above (Picture'First (2) );
W13 := Above (Picture'First (2) + 1);
W21 := (others => 0.0); -- The middle row
W22 := Get (I, Picture'First (2) );
W23 := Get (I, Picture'First (2) + 1);
W31 := (others => 0.0); -- The bottom row
W32 := Get (I+1, Picture'First (2) );
W33 := Get (I+1, Picture'First (2) + 1);
for J in Picture'Range (2) loop
This :=
W11 * K (-1, -1) + W12 * K (-1, 0) + W13 * K (-1, 1) +
W21 * K ( 0, -1) + W22 * K ( 0, 0) + W23 * K ( 0, 1) +
W31 * K ( 1, -1) + W32 * K ( 1, 0) + W33 * K ( 1, 1);
Above (J-1) := W21;
W11 := W12; W12 := W13; W13 := Above (J+1); -- Shift the window
W21 := W22; W22 := W23; W23 := Get (I, J+1);
W31 := W32; W32 := W23; W33 := Get (I+1, J+1);
Picture (I, J) := To_Pixel (This);
end loop;
Above (Picture'Last (2)) := W21;
end loop;
end Filter;

Example of use:

   F1, F2 : File_Type;
begin
Open (F1, In_File, "city.ppm");
declare
X : Image := Get_PPM (F1);
begin
Close (F1);
Create (F2, Out_File, "city_sharpen.ppm");
Filter (X, ((-1.0, -1.0, -1.0), (-1.0, 9.0, -1.0), (-1.0, -1.0, -1.0)));
Put_PPM (F2, X);
end;
Close (F2);

[edit] BBC BASIC

Original bbc.jpg
Sharpened bbc.jpg
      Width% = 200
Height% = 200
 
DIM out&(Width%-1, Height%-1, 2)
 
VDU 23,22,Width%;Height%;8,16,16,128
*DISPLAY Lena
OFF
 
DIM filter%(2, 2)
filter%() = -1, -1, -1, -1, 12, -1, -1, -1, -1
 
REM Do the convolution:
FOR Y% = 1 TO Height%-2
FOR X% = 1 TO Width%-2
R% = 0 : G% = 0 : B% = 0
FOR I% = -1 TO 1
FOR J% = -1 TO 1
C% = TINT((X%+I%)*2, (Y%+J%)*2)
F% = filter%(I%+1,J%+1)
R% += F% * (C% AND &FF)
G% += F% * (C% >> 8 AND &FF)
B% += F% * (C% >> 16)
NEXT
NEXT
IF R% < 0 R% = 0 ELSE IF R% > 1020 R% = 1020
IF G% < 0 G% = 0 ELSE IF G% > 1020 G% = 1020
IF B% < 0 B% = 0 ELSE IF B% > 1020 B% = 1020
out&(X%, Y%, 0) = R% / 4 + 0.5
out&(X%, Y%, 1) = G% / 4 + 0.5
out&(X%, Y%, 2) = B% / 4 + 0.5
NEXT
NEXT Y%
 
REM Display:
GCOL 1
FOR Y% = 0 TO Height%-1
FOR X% = 0 TO Width%-1
COLOUR 1, out&(X%,Y%,0), out&(X%,Y%,1), out&(X%,Y%,2)
LINE X%*2,Y%*2,X%*2,Y%*2
NEXT
NEXT Y%
 
REPEAT
WAIT 1
UNTIL FALSE

[edit] C

Interface:

image filter(image img, double *K, int Ks, double, double);

The implementation (the Ks argument is so that 1 specifies a 3×3 matrix, 2 a 5×5 matrix ... N a (2N+1)×(2N+1) matrix).

#include "imglib.h"
 
inline static color_component GET_PIXEL_CHECK(image img, int x, int y, int l) {
if ( (x<0) || (x >= img->width) || (y<0) || (y >= img->height) ) return 0;
return GET_PIXEL(img, x, y)[l];
}
 
image filter(image im, double *K, int Ks, double divisor, double offset)
{
image oi;
unsigned int ix, iy, l;
int kx, ky;
double cp[3];
 
oi = alloc_img(im->width, im->height);
if ( oi != NULL ) {
for(ix=0; ix < im->width; ix++) {
for(iy=0; iy < im->height; iy++) {
cp[0] = cp[1] = cp[2] = 0.0;
for(kx=-Ks; kx <= Ks; kx++) {
for(ky=-Ks; ky <= Ks; ky++) {
for(l=0; l<3; l++)
cp[l] += (K[(kx+Ks) +
(ky+Ks)*(2*Ks+1)]/divisor) *
((double)GET_PIXEL_CHECK(im, ix+kx, iy+ky, l)) + offset;
}
}
for(l=0; l<3; l++)
cp[l] = (cp[l]>255.0) ? 255.0 : ((cp[l]<0.0) ? 0.0 : cp[l]) ;
put_pixel_unsafe(oi, ix, iy,
(color_component)cp[0],
(color_component)cp[1],
(color_component)cp[2]);
}
}
return oi;
}
return NULL;
}

Usage example:

The read_image function is from here.

#include <stdio.h>
#include "imglib.h"
 
const char *input = "Lenna100.jpg";
const char *output = "filtered_lenna%d.ppm";
 
double emboss_kernel[3*3] = {
-2., -1., 0.,
-1., 1., 1.,
0., 1., 2.,
};
 
double sharpen_kernel[3*3] = {
-1.0, -1.0, -1.0,
-1.0, 9.0, -1.0,
-1.0, -1.0, -1.0
};
double sobel_emboss_kernel[3*3] = {
-1., -2., -1.,
0., 0., 0.,
1., 2., 1.,
};
double box_blur_kernel[3*3] = {
1.0, 1.0, 1.0,
1.0, 1.0, 1.0,
1.0, 1.0, 1.0,
};
 
double *filters[4] = {
emboss_kernel, sharpen_kernel, sobel_emboss_kernel, box_blur_kernel
};
const double filter_params[2*4] = {
1.0, 0.0,
1.0, 0.0,
1.0, 0.5,
9.0, 0.0
};
 
int main()
{
image ii, oi;
int i;
char lennanames[30];
 
ii = read_image(input);
if ( ii != NULL ) {
for(i=0; i<4; i++) {
sprintf(lennanames, output, i);
oi = filter(ii, filters[i], 1, filter_params[2*i], filter_params[2*i+1]);
if ( oi != NULL ) {
FILE *outfh = fopen(lennanames, "w");
if ( outfh != NULL ) {
output_ppm(outfh, oi);
fclose(outfh);
} else { fprintf(stderr, "out err %s\n", output); }
free_img(oi);
} else { fprintf(stderr, "err creating img filters %d\n", i); }
}
free_img(ii);
} else { fprintf(stderr, "err reading %s\n", input); }
}

[edit] D

This requires the module from the Grayscale Image Task.

import std.string, std.math, std.algorithm, grayscale_image;
 
struct ConvolutionFilter {
double[][] kernel;
double divisor, offset_;
string name;
}
 
 
Image!Color convolve(Color)(in Image!Color im,
in ConvolutionFilter filter)
pure nothrow in {
assert(im !is null);
assert(!isnan(filter.divisor) && !isnan(filter.offset_));
assert(filter.divisor != 0);
assert(filter.kernel.length > 0 && filter.kernel[0].length > 0);
foreach (const row; filter.kernel) // Is rectangular.
assert(row.length == filter.kernel[0].length);
assert(filter.kernel.length % 2 == 1); // Odd sized kernel.
assert(filter.kernel[0].length % 2 == 1);
assert(im.ny >= filter.kernel.length);
assert(im.nx >= filter.kernel[0].length);
} out(result) {
assert(result !is null);
assert(result.nx == im.nx && result.ny == im.ny);
} body {
immutable knx2 = filter.kernel[0].length / 2;
immutable kny2 = filter.kernel.length / 2;
auto io = new Image!Color(im.nx, im.ny);
 
static if (is(Color == RGB))
alias CT = typeof(Color.r); // Component type.
else static if (is(typeof(Color.c)))
alias CT = typeof(Color.c);
else
alias CT = Color;
 
foreach (immutable y; kny2 .. im.ny - kny2) {
foreach (immutable x; knx2 .. im.nx - knx2) {
static if (is(Color == RGB))
double[3] total = 0.0;
else
double total = 0.0;
 
foreach (immutable sy, const kRow; filter.kernel) {
foreach (immutable sx, immutable k; kRow) {
immutable p = im[x + sx - knx2, y + sy - kny2];
static if (is(Color == RGB)) {
total[0] += p.r * k;
total[1] += p.g * k;
total[2] += p.b * k;
} else {
total += p * k;
}
}
}
 
immutable D = filter.divisor;
immutable O = filter.offset_ * CT.max;
static if (is(Color == RGB)) {
io[x, y] = Color(
cast(CT)min(max(total[0]/ D + O, CT.min), CT.max),
cast(CT)min(max(total[1]/ D + O, CT.min), CT.max),
cast(CT)min(max(total[2]/ D + O, CT.min), CT.max));
} else static if (is(typeof(Color.c))) {
io[x, y] = Color(
cast(CT)min(max(total / D + O, CT.min), CT.max));
} else {
// If Color doesn't have a 'c' field, then Color is
// assumed to be a built-in type.
io[x, y] =
cast(CT)min(max(total / D + O, CT.min), CT.max);
}
}
}
 
return io;
}
 
 
void main() {
immutable ConvolutionFilter[] filters = [
{[[-2.0, -1.0, 0.0],
[-1.0, 1.0, 1.0],
[ 0.0, 1.0, 2.0]], divisor:1.0, offset_:0.0, name:"Emboss"},
 
{[[-1.0, -1.0, -1.0],
[-1.0, 9.0, -1.0],
[-1.0, -1.0, -1.0]], divisor:1.0, 0.0, "Sharpen"},
 
{[[-1.0, -2.0, -1.0],
[ 0.0, 0.0, 0.0],
[ 1.0, 2.0, 1.0]], divisor:1.0, 0.5, "Sobel_emboss"},
 
{[[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]], divisor:9.0, 0.0, "Box_blur"},
 
{[[1, 4, 7, 4, 1],
[4, 16, 26, 16, 4],
[7, 26, 41, 26, 7],
[4, 16, 26, 16, 4],
[1, 4, 7, 4, 1]], divisor:273, 0.0, "Gaussian_blur"}];
 
Image!RGB im;
im.loadPPM6("Lenna100.ppm");
 
foreach (immutable filter; filters)
im.convolve(filter)
.savePPM6(format("lenna_%s.ppm", filter.name));
 
const img = im.rgb2grayImage();
foreach (immutable filter; filters)
img.convolve(filter)
.savePGM(format("lenna_gray_%s.ppm", filter.name));
}

[edit] Go

Using standard image library:

package main
 
import (
"fmt"
"image"
"image/color"
"image/jpeg"
"math"
"os"
)
 
// kf3 is a generic convolution 3x3 kernel filter that operatates on
// images of type image.Gray from the Go standard image library.
func kf3(k *[9]float64, src, dst *image.Gray) {
for y := src.Rect.Min.Y; y < src.Rect.Max.Y; y++ {
for x := src.Rect.Min.X; x < src.Rect.Max.X; x++ {
var sum float64
var i int
for yo := y - 1; yo <= y+1; yo++ {
for xo := x - 1; xo <= x+1; xo++ {
if (image.Point{xo, yo}).In(src.Rect) {
sum += k[i] * float64(src.At(xo, yo).(color.Gray).Y)
} else {
sum += k[i] * float64(src.At(x, y).(color.Gray).Y)
}
i++
}
}
dst.SetGray(x, y,
color.Gray{uint8(math.Min(255, math.Max(0, sum)))})
}
}
}
 
var blur = [9]float64{
1. / 9, 1. / 9, 1. / 9,
1. / 9, 1. / 9, 1. / 9,
1. / 9, 1. / 9, 1. / 9}
 
// blurY example function applies blur kernel to Y channel
// of YCbCr image using generic kernel filter function kf3
func blurY(src *image.YCbCr) *image.YCbCr {
dst := *src
 
// catch zero-size image here
if src.Rect.Max.X == src.Rect.Min.X || src.Rect.Max.Y == src.Rect.Min.Y {
return &dst
}
 
// pass Y channels as gray images
srcGray := image.Gray{src.Y, src.YStride, src.Rect}
dstGray := srcGray
dstGray.Pix = make([]uint8, len(src.Y))
kf3(&blur, &srcGray, &dstGray) // call generic convolution function
 
// complete result
dst.Y = dstGray.Pix // convolution result
dst.Cb = append([]uint8{}, src.Cb...) // Cb, Cr are just copied
dst.Cr = append([]uint8{}, src.Cr...)
return &dst
}
 
func main() {
// Example file used here is Lenna100.jpg from the task "Percentage
// difference between images"
f, err := os.Open("Lenna100.jpg")
if err != nil {
fmt.Println(err)
return
}
img, err := jpeg.Decode(f)
if err != nil {
fmt.Println(err)
return
}
f.Close()
y, ok := img.(*image.YCbCr)
if !ok {
fmt.Println("expected color jpeg")
return
}
f, err = os.Create("blur.jpg")
if err != nil {
fmt.Println(err)
return
}
err = jpeg.Encode(f, blurY(y), &jpeg.Options{90})
if err != nil {
fmt.Println(err)
}
}

Alternative version, building on code from bitmap task.

New function for raster package:

package raster
 
import "math"
 
func (g *Grmap) KernelFilter3(k []float64) *Grmap {
if len(k) != 9 {
return nil
}
r := NewGrmap(g.cols, g.rows)
r.Comments = append([]string{}, g.Comments...)
// Filter edge pixels with minimal code.
// Execution time per pixel is high but there are few edge pixels
// relative to the interior.
o3 := [][]int{
{-1, -1}, {0, -1}, {1, -1},
{-1, 0}, {0, 0}, {1, 0},
{-1, 1}, {0, 1}, {1, 1}}
edge := func(x, y int) uint16 {
var sum float64
for i, o := range o3 {
c, ok := g.GetPx(x+o[0], y+o[1])
if !ok {
c = g.pxRow[y][x]
}
sum += float64(c) * k[i]
}
return uint16(math.Min(math.MaxUint16, math.Max(0,sum)))
}
for x := 0; x < r.cols; x++ {
r.pxRow[0][x] = edge(x, 0)
r.pxRow[r.rows-1][x] = edge(x, r.rows-1)
}
for y := 1; y < r.rows-1; y++ {
r.pxRow[y][0] = edge(0, y)
r.pxRow[y][r.cols-1] = edge(r.cols-1, y)
}
if r.rows < 3 || r.cols < 3 {
return r
}
 
// Interior pixels can be filtered much more efficiently.
otr := -g.cols + 1
obr := g.cols + 1
z := g.cols + 1
c2 := g.cols - 2
for y := 1; y < r.rows-1; y++ {
tl := float64(g.pxRow[y-1][0])
tc := float64(g.pxRow[y-1][1])
tr := float64(g.pxRow[y-1][2])
ml := float64(g.pxRow[y][0])
mc := float64(g.pxRow[y][1])
mr := float64(g.pxRow[y][2])
bl := float64(g.pxRow[y+1][0])
bc := float64(g.pxRow[y+1][1])
br := float64(g.pxRow[y+1][2])
for x := 1; ; x++ {
r.px[z] = uint16(math.Min(math.MaxUint16, math.Max(0,
tl*k[0] + tc*k[1] + tr*k[2] +
ml*k[3] + mc*k[4] + mr*k[5] +
bl*k[6] + bc*k[7] + br*k[8])))
if x == c2 {
break
}
z++
tl, tc, tr = tc, tr, float64(g.px[z+otr])
ml, mc, mr = mc, mr, float64(g.px[z+1])
bl, bc, br = bc, br, float64(g.px[z+obr])
}
z += 3
}
return r
}

Demonstration program:

package main
 
// Files required to build supporting package raster are found in:
// * This task (immediately above)
// * Bitmap
// * Grayscale image
// * Read a PPM file
// * Write a PPM file
 
import (
"fmt"
"raster"
)
 
var blur = []float64{
1./9, 1./9, 1./9,
1./9, 1./9, 1./9,
1./9, 1./9, 1./9}
 
var sharpen = []float64{
-1, -1, -1,
-1, 9, -1,
-1, -1, -1}
 
func main() {
// Example file used here is Lenna100.jpg from the task "Percentage
// difference between images" converted with with the command
// convert Lenna100.jpg -colorspace gray Lenna100.ppm
b, err := raster.ReadPpmFile("Lenna100.ppm")
if err != nil {
fmt.Println(err)
return
}
g0 := b.Grmap()
g1 := g0.KernelFilter3(blur)
err = g1.Bitmap().WritePpmFile("blur.ppm")
if err != nil {
fmt.Println(err)
}
}

[edit] J

NB. pad the first n dimensions of an array with zeros
NB. (increasing all dimensions by 1 less than the kernel size)
pad=: adverb define
adj1=: <.m%2
adj2=: m-1
(-@(adj2 + ]) {. (adj1 + ]) {. [) (#m) {. $
)
 
kernel_filter=: adverb define
[: ,/"(-#$m) ($m) +/@(,/^:(_1+#$m))@:*&m;._3 ($m)pad
)


This code assumes that the leading dimensions of the array represent pixels and any trailing dimensions represent structure to be preserved (this is a fairly common approach and matches the J implementation at Basic bitmap storage).

Example use:

   NB. kernels borrowed from C and TCL implementations
   sharpen_kernel=: _1+10*4=i.3 3
   blur_kernel=: 3 3$%9
   emboss_kernel=: _2 _1 0,_1 1 1,:0 1 2
   sobel_emboss_kernel=: _1 _2 _1,0,:1 2 1
   'blurred.ppm' writeppm~ blur_kernel kernel_filter readppm 'original.ppm'

[edit] Java

Code:

import java.awt.image.*;
import java.io.File;
import java.io.IOException;
import javax.imageio.*;
 
public class ImageConvolution
{
public static class ArrayData
{
public final int[] dataArray;
public final int width;
public final int height;
 
public ArrayData(int width, int height)
{
this(new int[width * height], width, height);
}
 
public ArrayData(int[] dataArray, int width, int height)
{
this.dataArray = dataArray;
this.width = width;
this.height = height;
}
 
public int get(int x, int y)
{ return dataArray[y * width + x]; }
 
public void set(int x, int y, int value)
{ dataArray[y * width + x] = value; }
}
 
private static int bound(int value, int endIndex)
{
if (value < 0)
return 0;
if (value < endIndex)
return value;
return endIndex - 1;
}
 
public static ArrayData convolute(ArrayData inputData, ArrayData kernel, int kernelDivisor)
{
int inputWidth = inputData.width;
int inputHeight = inputData.height;
int kernelWidth = kernel.width;
int kernelHeight = kernel.height;
if ((kernelWidth <= 0) || ((kernelWidth & 1) != 1))
throw new IllegalArgumentException("Kernel must have odd width");
if ((kernelHeight <= 0) || ((kernelHeight & 1) != 1))
throw new IllegalArgumentException("Kernel must have odd height");
int kernelWidthRadius = kernelWidth >>> 1;
int kernelHeightRadius = kernelHeight >>> 1;
 
ArrayData outputData = new ArrayData(inputWidth, inputHeight);
for (int i = inputWidth - 1; i >= 0; i--)
{
for (int j = inputHeight - 1; j >= 0; j--)
{
double newValue = 0.0;
for (int kw = kernelWidth - 1; kw >= 0; kw--)
for (int kh = kernelHeight - 1; kh >= 0; kh--)
newValue += kernel.get(kw, kh) * inputData.get(
bound(i + kw - kernelWidthRadius, inputWidth),
bound(j + kh - kernelHeightRadius, inputHeight));
outputData.set(i, j, (int)Math.round(newValue / kernelDivisor));
}
}
return outputData;
}
 
public static ArrayData[] getArrayDatasFromImage(String filename) throws IOException
{
BufferedImage inputImage = ImageIO.read(new File(filename));
int width = inputImage.getWidth();
int height = inputImage.getHeight();
int[] rgbData = inputImage.getRGB(0, 0, width, height, null, 0, width);
ArrayData reds = new ArrayData(width, height);
ArrayData greens = new ArrayData(width, height);
ArrayData blues = new ArrayData(width, height);
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
int rgbValue = rgbData[y * width + x];
reds.set(x, y, (rgbValue >>> 16) & 0xFF);
greens.set(x, y, (rgbValue >>> 8) & 0xFF);
blues.set(x, y, rgbValue & 0xFF);
}
}
return new ArrayData[] { reds, greens, blues };
}
 
public static void writeOutputImage(String filename, ArrayData[] redGreenBlue) throws IOException
{
ArrayData reds = redGreenBlue[0];
ArrayData greens = redGreenBlue[1];
ArrayData blues = redGreenBlue[2];
BufferedImage outputImage = new BufferedImage(reds.width, reds.height,
BufferedImage.TYPE_INT_ARGB);
for (int y = 0; y < reds.height; y++)
{
for (int x = 0; x < reds.width; x++)
{
int red = bound(reds.get(x, y), 256);
int green = bound(greens.get(x, y), 256);
int blue = bound(blues.get(x, y), 256);
outputImage.setRGB(x, y, (red << 16) | (green << 8) | blue | -0x01000000);
}
}
ImageIO.write(outputImage, "PNG", new File(filename));
return;
}
 
public static void main(String[] args) throws IOException
{
int kernelWidth = Integer.parseInt(args[2]);
int kernelHeight = Integer.parseInt(args[3]);
int kernelDivisor = Integer.parseInt(args[4]);
System.out.println("Kernel size: " + kernelWidth + "x" + kernelHeight +
", divisor=" + kernelDivisor);
int y = 5;
ArrayData kernel = new ArrayData(kernelWidth, kernelHeight);
for (int i = 0; i < kernelHeight; i++)
{
System.out.print("[");
for (int j = 0; j < kernelWidth; j++)
{
kernel.set(j, i, Integer.parseInt(args[y++]));
System.out.print(" " + kernel.get(j, i) + " ");
}
System.out.println("]");
}
 
ArrayData[] dataArrays = getArrayDatasFromImage(args[0]);
for (int i = 0; i < dataArrays.length; i++)
dataArrays[i] = convolute(dataArrays[i], kernel, kernelDivisor);
writeOutputImage(args[1], dataArrays);
return;
}
}


Output from example pentagon image
Example 5x5 Gaussian blur, using Pentagon.png from the Hough transform task:
java ImageConvolution pentagon.png JavaImageConvolution.png 5 5 273 1 4 7 4 1  4 16 26 16 4  7 26 41 26 7  4 16 26 16 4  1 4 7 4 1
Kernel size: 5x5, divisor=273
[ 1  4  7  4  1 ]
[ 4  16  26  16  4 ]
[ 7  26  41  26  7 ]
[ 4  16  26  16  4 ]
[ 1  4  7  4  1 ]

[edit] Liberty BASIC

In the following a 128x128 bmp file is loaded and its brightness values are read into an array.
We then convolve it with a 'sharpen' 3x3 matrix. Results are shown directly on screen.
NB Things like convolution would be best done by combining LB with ImageMagick, which is easily called from LB.

 
dim result( 300, 300), image( 300, 300), mask( 100, 100)
w =128
h =128
 
nomainwin
 
WindowWidth = 460
WindowHeight = 210
 
open "Convolution" for graphics_nsb_nf as #w
 
#w "trapclose [quit]"
 
#w "down ; fill darkblue"
 
hw = hwnd( #w)
calldll #user32,"GetDC", hw as ulong, hdc as ulong
 
loadbmp "img", "alpha25.bmp"' 128x128 pixels
#w "drawbmp img 20, 20"
 
#w "up ; color white ; goto 292 20 ; down ; box 420 148"
#w "up ; goto 180 60 ; down ; backcolor darkblue ; color cyan"
#w "\"; "Convolved with"
 
for y =0 to 127 ' fill in the input matrix
for x =0 to 127
xx =x + 20
yy =y + 20
CallDLL #gdi32, "GetPixel", hdc as uLong, xx as long, yy as long, pixcol as ulong
call getRGB pixcol, b, g, r
image( x, y) =b
'#w "color "; image( x, y); " 0 "; 255 -image( x, y)
'#w "set "; x + 20; " "; y +20 +140
next x
next y
 
#w "flush"
print " Input matrix filled."
 
#w "size 8"
for y =0 to 2 ' fill in the mask matrix
for x =0 to 2
read mask
mask( x, y) =mask
if mask = ( 0 -1) then #w "color yellow" else #w "color red"
#w "set "; 8 *x +200; " "; 8 *y +80
next x
next y
data -1,-1,-1,-1,9,-1,-1,-1,-1
 
#w "flush"
print " Mask matrix filled."
 
#w "size 1"
mxx =0: mnn =0
 
for x =0 to 127 -2 ' since any further overlaps image edge
for y =0 to 127 -2
result( x, y) =0
for kx =0 to 2
for ky =0 to 2
result( x, y) =result( x, y) +image( x +kx, y +ky) *mask( kx, ky)
next ky
if mxx <result( x, y) then mxx =result( x, y)
if mnn >result( x, y) then mnn =result( x, y)
next kx
scan
next y
next x
 
range =mxx -mnn
for x =0 to 127 -2
for y =0 to 127 -2
c =int( 255 *( result( x, y) -mnn) /range)
'#w "color "; c; " "; c; " "; c
if c >128 then #w "color white" else #w "color black"
#w "set "; x +292 +1; " "; y +20 +1
scan
next y
next x
#w "flush"
 
wait
 
sub getRGB pixcol, byref r, byref g, byref b
b = int( pixcol / (256 *256))
g = int( ( pixcol - b *256 *256) / 256)
r = int( pixcol - b *256 *256 - g *256)
end sub
 
[quit]
close #w
CallDLL #user32, "ReleaseDC", hw as ulong, hdc as ulong
end
 
Screenview is available at [[1]]


[edit] Mathematica

Most image processing functions introduced in Mathematica 7

img = Import[NotebookDirectory[] <> "Lenna50.jpg"];
kernel = {{0, -1, 0}, {-1, 4, -1}, {0, -1, 0}};
ImageConvolve[img, kernel]
ImageConvolve[img, GaussianMatrix[35] ]
ImageConvolve[img, BoxMatrix[1] ]

[edit] MATLAB

The built-in function conv2 handles the basic convolution. Below is a program that has several more options that may be useful in different image processing applications (see comments under convImage for specifics).

function testConvImage
Im = [1 2 1 5 5 ; ...
1 2 7 9 9 ; ...
5 5 5 5 5 ; ...
5 2 2 2 2 ; ...
1 1 1 1 1 ]; % Sample image for example illustration only
Ker = [1 2 1 ; ...
2 4 2 ; ...
1 2 1 ]; % Gaussian smoothing (without normalizing)
fprintf('Original image:\n')
disp(Im)
fprintf('Original kernal:\n')
disp(Ker)
fprintf('Padding with zeroes:\n')
disp(convImage(Im, Ker, 'zeros'))
fprintf('Padding with fives:\n')
disp(convImage(Im, Ker, 'value', 5))
fprintf('Duplicating border pixels to pad image:\n')
disp(convImage(Im, Ker, 'extend'))
fprintf('Renormalizing kernal and using only values within image:\n')
disp(convImage(Im, Ker, 'partial'))
fprintf('Only processing inner (non-border) pixels:\n')
disp(convImage(Im, Ker, 'none'))
% Ker = [1 2 1 ; ...
% 2 4 2 ; ...
% 1 2 1 ]./16;
% Im = imread('testConvImageTestImage.png', 'png');
% figure
% imshow(imresize(Im, 10))
% title('Original image')
% figure
% imshow(imresize(convImage(Im, Ker, 'zeros'), 10))
% title('Padding with zeroes')
% figure
% imshow(imresize(convImage(Im, Ker, 'value', 50), 10))
% title('Padding with fifty: 50')
% figure
% imshow(imresize(convImage(Im, Ker, 'extend'), 10))
% title('Duplicating border pixels to pad image')
% figure
% imshow(imresize(convImage(Im, Ker, 'partial'), 10))
% title('Renormalizing kernal and using only values within image')
% figure
% imshow(imresize(convImage(Im, Ker, 'none'), 10))
% title('Only processing inner (non-border) pixels')
end
 
function ImOut = convImage(Im, Ker, varargin)
% ImOut = convImage(Im, Ker)
% Filters an image using sliding-window kernal convolution.
% Convolution is done layer-by-layer. Use rgb2gray if single-layer needed.
% Zero-padding convolution will be used if no border handling is specified.
% Im - Array containing image data (output from imread)
% Ker - 2-D array to convolve image, needs odd number of rows and columns
% ImOut - Filtered image, same dimensions and datatype as Im
%
% ImOut = convImage(Im, Ker, 'zeros')
% Image will be padded with zeros when calculating convolution
% (useful for magnitude calculations).
%
% ImOut = convImage(Im, Ker, 'value', padVal)
% Image will be padded with padVal when calculating convolution
% (possibly useful for emphasizing certain data with unusual kernal)
%
% ImOut = convImage(Im, Ker, 'extend')
% Image will be padded with the value of the closest image pixel
% (useful for smoothing or blurring filters).
%
% ImOut = convImage(Im, Ker, 'partial')
% Image will not be padded. Borders will be convoluted with only valid pixels,
% and convolution matrix will be renormalized counting only the pixels within
% the image (also useful for smoothing or blurring filters).
%
% ImOut = convImage(Im, Ker, 'none')
% Image will not be padded. Convolution will only be applied to inner pixels
% (useful for edge and corner detection filters)
 
% Handle input
if mod(size(Ker, 1), 2) ~= 1 || mod(size(Ker, 2), 2) ~= 1
eid = sprintf('%s:evenRowsCols', mfilename);
error(eid,'''Ker'' parameter must have odd number of rows and columns.')
elseif nargin > 4
eid = sprintf('%s:maxrhs', mfilename);
error(eid, 'Too many input arguments.');
elseif nargin == 4 && ~strcmp(varargin{1}, 'value')
eid = sprintf('%s:invalidParameterCombination', mfilename);
error(eid, ['The ''padVal'' parameter is only valid with the ' ...
'''value'' option.'])
elseif nargin < 4 && strcmp(varargin{1}, 'value')
eid = sprintf('%s:minrhs', mfilename);
error(eid, 'Not enough input arguments.')
elseif nargin < 3
method = 'zeros';
else
method = lower(varargin{1});
if ~any(strcmp(method, {'zeros' 'value' 'extend' 'partial' 'none'}))
eid = sprintf('%s:invalidParameter', mfilename);
error(eid, 'Invalid option parameter. Must be one of:%s', ...
sprintf('\n\t\t%s', ...
'zeros', 'value', 'extend', 'partial', 'none'))
end
end
 
% Gather information and prepare for convolution
[nImRows, nImCols, nImLayers] = size(Im);
classIm = class(Im);
Im = double(Im);
ImOut = zeros(nImRows, nImCols, nImLayers);
[nKerRows, nKerCols] = size(Ker);
nPadRows = nImRows+nKerRows-1;
nPadCols = nImCols+nKerCols-1;
padH = (nKerRows-1)/2;
padW = (nKerCols-1)/2;
 
% Convolute on a layer-by-layer basis
for k = 1:nImLayers
if strcmp(method, 'zeros')
ImOut(:, :, k) = conv2(Im(:, :, k), Ker, 'same');
elseif strcmp(method, 'value')
padding = varargin{2}.*ones(nPadRows, nPadCols);
padding(padH+1:end-padH, padW+1:end-padW) = Im(:, :, k);
ImOut(:, :, k) = conv2(padding, Ker, 'valid');
elseif strcmp(method, 'extend')
padding = zeros(nPadRows, nPadCols);
padding(padH+1:end-padH, padW+1:end-padW) = Im(:, :, k); % Middle
padding(1:padH, 1:padW) = Im(1, 1, k); % TopLeft
padding(end-padH+1:end, 1:padW) = Im(end, 1, k); % BotLeft
padding(1:padH, end-padW+1:end) = Im(1, end, k); % TopRight
padding(end-padH+1:end, end-padW+1:end) = Im(end, end, k);% BotRight
padding(padH+1:end-padH, 1:padW) = ...
repmat(Im(:, 1, k), 1, padW); % Left
padding(padH+1:end-padH, end-padW+1:end) = ...
repmat(Im(:, end, k), 1, padW); % Right
padding(1:padH, padW+1:end-padW) = ...
repmat(Im(1, :, k), padH, 1); % Top
padding(end-padH+1:end, padW+1:end-padW) = ...
repmat(Im(end, :, k), padH, 1); % Bottom
ImOut(:, :, k) = conv2(padding, Ker, 'valid');
elseif strcmp(method, 'partial')
ImOut(padH+1:end-padH, padW+1:end-padW, k) = ...
conv2(Im(:, :, k), Ker, 'valid'); % Middle
unprocessed = true(nImRows, nImCols);
unprocessed(padH+1:end-padH, padW+1:end-padW) = false; % Border
for r = 1:nImRows
for c = 1:nImCols
if unprocessed(r, c)
limitedIm = Im(max(1, r-padH):min(nImRows, r+padH), ...
max(1, c-padW):min(nImCols, c+padW), k);
limitedKer = Ker(max(1, 2-r+padH): ...
min(nKerRows, nKerRows+nImRows-r-padH), ...
max(1, 2-c+padW):...
min(nKerCols, nKerCols+nImCols-c-padW));
limitedKer = limitedKer.*sum(Ker(:))./ ...
sum(limitedKer(:));
ImOut(r, c, k) = sum(sum(limitedIm.*limitedKer));
end
end
end
else % method is 'none'
ImOut(:, :, k) = Im(:, :, k);
ImOut(padH+1:end-padH, padW+1:end-padW, k) = ...
conv2(Im(:, :, k), Ker, 'valid');
end
end
 
% Convert back to former image data type
ImOut = cast(ImOut, classIm);
end
Output:
Original image:
     1     2     1     5     5
     1     2     7     9     9
     5     5     5     5     5
     5     2     2     2     2
     1     1     1     1     1

Original kernal:
     1     2     1
     2     4     2
     1     2     1

Padding with zeroes:
    12    24    43    66    57
    27    50    79   104    84
    46    63    73    82    63
    42    46    40    40    30
    18    19    16    16    12

Padding with fives:
    47    44    63    86    92
    47    50    79   104   104
    66    63    73    82    83
    62    46    40    40    50
    53    39    36    36    47

Duplicating border pixels to pad image:
    20    30    52    82    96
    35    50    79   104   112
    62    63    73    82    84
    58    46    40    40    40
    29    23    20    20    20

Renormalizing kernal and using only values within image:
   21.3333   32.0000   57.3333   88.0000  101.3333
   36.0000   50.0000   79.0000  104.0000  112.0000
   61.3333   63.0000   73.0000   82.0000   84.0000
   56.0000   46.0000   40.0000   40.0000   40.0000
   32.0000   25.3333   21.3333   21.3333   21.3333

Only processing inner (non-border) pixels:
     1     2     1     5     5
     1    50    79   104     9
     5    63    73    82     5
     5    46    40    40     2
     1     1     1     1     1

[edit] OCaml

let get_rgb img x y =
let _, r_channel,_,_ = img in
let width = Bigarray.Array2.dim1 r_channel
and height = Bigarray.Array2.dim2 r_channel in
if (x < 0) || (x >= width) then (0,0,0) else
if (y < 0) || (y >= height) then (0,0,0) else (* feed borders with black *)
get_pixel img x y
 
 
let convolve_get_value img kernel divisor offset = fun x y ->
let sum_r = ref 0.0
and sum_g = ref 0.0
and sum_b = ref 0.0 in
 
for i = -1 to 1 do
for j = -1 to 1 do
let r, g, b = get_rgb img (x+i) (y+j) in
sum_r := !sum_r +. kernel.(j+1).(i+1) *. (float r);
sum_g := !sum_g +. kernel.(j+1).(i+1) *. (float g);
sum_b := !sum_b +. kernel.(j+1).(i+1) *. (float b);
done;
done;
( !sum_r /. divisor +. offset,
!sum_g /. divisor +. offset,
!sum_b /. divisor +. offset )
 
 
let color_to_int (r,g,b) =
(truncate r,
truncate g,
truncate b)
 
let bounded (r,g,b) =
((max 0 (min r 255)),
(max 0 (min g 255)),
(max 0 (min b 255)))
 
 
let convolve_value ~img ~kernel ~divisor ~offset =
let _, r_channel,_,_ = img in
let width = Bigarray.Array2.dim1 r_channel
and height = Bigarray.Array2.dim2 r_channel in
 
let res = new_img ~width ~height in
 
let conv = convolve_get_value img kernel divisor offset in
 
for y = 0 to pred height do
for x = 0 to pred width do
let color = conv x y in
let color = color_to_int color in
put_pixel res (bounded color) x y;
done;
done;
(res)
let emboss img =
let kernel = [|
[| -2.; -1.; 0. |];
[| -1.; 1.; 1. |];
[| 0.; 1.; 2. |];
|] in
convolve_value ~img ~kernel ~divisor:1.0 ~offset:0.0;
;;
 
let sharpen img =
let kernel = [|
[| -1.; -1.; -1. |];
[| -1.; 9.; -1. |];
[| -1.; -1.; -1. |];
|] in
convolve_value ~img ~kernel ~divisor:1.0 ~offset:0.0;
;;
 
let sobel_emboss img =
let kernel = [|
[| -1.; -2.; -1. |];
[| 0.; 0.; 0. |];
[| 1.; 2.; 1. |];
|] in
convolve_value ~img ~kernel ~divisor:1.0 ~offset:0.5;
;;
 
let box_blur img =
let kernel = [|
[| 1.; 1.; 1. |];
[| 1.; 1.; 1. |];
[| 1.; 1.; 1. |];
|] in
convolve_value ~img ~kernel ~divisor:9.0 ~offset:0.0;
;;

[edit] Octave

Use package Image

function [r, g, b] = rgbconv2(a, c)
r = im2uint8(mat2gray(conv2(a(:,:,1), c)));
g = im2uint8(mat2gray(conv2(a(:,:,2), c)));
b = im2uint8(mat2gray(conv2(a(:,:,3), c)));
endfunction
 
im = jpgread("Lenna100.jpg");
emboss = [-2, -1, 0;
-1, 1, 1;
0, 1, 2 ];
sobel = [-1., -2., -1.;
0., 0., 0.;
1., 2., 1. ];
sharpen = [ -1.0, -1.0, -1.0;
-1.0, 9.0, -1.0;
-1.0, -1.0, -1.0 ];
 
[r, g, b] = rgbconv2(im, emboss);
jpgwrite("LennaEmboss.jpg", r, g, b, 100);
[r, g, b] = rgbconv2(im, sobel);
jpgwrite("LennaSobel.jpg", r, g, b, 100);
[r, g, b] = rgbconv2(im, sharpen);
jpgwrite("LennaSharpen.jpg", r, g, b, 100);

[edit] PicoLisp

(scl 3)
 
(de ppmConvolution (Ppm Kernel)
(let (Len (length (car Kernel)) Radius (/ Len 2))
(make
(chain (head Radius Ppm))
(for (Y Ppm T (cdr Y))
(NIL (nth Y Len)
(chain (tail Radius Y)) )
(link
(make
(chain (head Radius (get Y (inc Radius))))
(for (X (head Len Y) T)
(NIL (nth X 1 Len)
(chain (tail Radius (get X (inc Radius)))) )
(link
(make
(for C 3
(let Val 0
(for K Len
(for L Len
(inc 'Val
(* (get X K L C) (get Kernel K L)) ) ) )
(link (min 255 (max 0 (*/ Val 1.0)))) ) ) ) )
(map pop X) ) ) ) ) ) ) )

Test using 'ppmRead' from Bitmap/Read a PPM file#PicoLisp and 'ppmWrite' from Bitmap/Write a PPM file#PicoLisp:

# Sharpen
(ppmWrite
   (ppmConvolution
      (ppmRead "Lenna100.ppm")
      '((-1.0 -1.0 -1.0) (-1.0 +9.0 -1.0) (-1.0 -1.0 -1.0)) )
   "a.ppm" )

# Blur
(ppmWrite
   (ppmConvolution
      (ppmRead "Lenna100.ppm")
      '((0.1 0.1 0.1) (0.1 0.1 0.1) (0.1 0.1 0.1)) )
   "b.ppm" )

[edit] Racket

This example uses typed/racket, since that gives access to inline-build-flomap, which delivers quite a performance boost over build-flomap.

271px-John_Constable_002.jpg convolve-etch-3x3.png


#lang typed/racket
(require images/flomap racket/flonum)
 
(provide flomap-convolve)
 
(: perfect-square? (Nonnegative-Fixnum -> (U Nonnegative-Fixnum #f)))
(define (perfect-square? n)
(define rt-n (integer-sqrt n))
(and (= n (sqr rt-n)) rt-n))
 
(: flomap-convolve (flomap FlVector -> flomap))
(define (flomap-convolve F K)
(unless (flomap? F) (error "arg1 not a flowmap"))
(unless (flvector? K) (error "arg2 not a flvector"))
(define R (perfect-square? (flvector-length K)))
(cond
[(not (and R (odd? R))) (error "K is not odd-sided square")]
[else
(define R/2 (quotient R 2))
(define R/-2 (quotient R -2))
(define-values (sz-w sz-h) (flomap-size F))
(define-syntax-rule (convolution c x y i)
(if (= 0 c)
(flomap-ref F c x y) ; c=3 is alpha channel
(for*/fold: : Flonum
((acc : Flonum 0.))
((k (in-range 0 (add1 R/2)))
(l (in-range 0 (add1 R/2)))
(kl (in-value (+ (* k R) l)))
(kx (in-value (+ x k R/-2)))
(ly (in-value (+ y l R/-2)))
#:when (< 0 kx (sub1 sz-w))
#:when (< 0 ly (sub1 sz-h)))
(+ acc (* (flvector-ref K kl) (flomap-ref F c kx ly))))))
 
(inline-build-flomap 4 sz-w sz-h convolution)]))
 
(module* test racket
(require racket/draw images/flomap racket/flonum (only-in 2htdp/image save-image))
(require (submod ".."))
(define flmp (bitmap->flomap (read-bitmap "jpg/271px-John_Constable_002.jpg")))
(save-image
(flomap->bitmap (flomap-convolve flmp (flvector 1.)))
"out/convolve-unit-1x1.png")
(save-image
(flomap->bitmap (flomap-convolve flmp (flvector 0. 0. 0. 0. 1. 0. 0. 0. 0.)))
"out/convolve-unit-3x3.png")
(save-image
(flomap->bitmap (flomap-convolve flmp (flvector -1. -1. -1. -1. 4. -1. -1. -1. -1.)))
"out/convolve-etch-3x3.png"))

[edit] Ruby

Translation of: Tcl
class Pixmap
# Apply a convolution kernel to a whole image
def convolute(kernel)
newimg = Pixmap.new(@width, @height)
pb = ProgressBar.new(@width) if $DEBUG
@width.times do |x|
@height.times do |y|
apply_kernel(x, y, kernel, newimg)
end
pb.update(x) if $DEBUG
end
pb.close if $DEBUG
newimg
end
 
# Applies a convolution kernel to produce a single pixel in the destination
def apply_kernel(x, y, kernel, newimg)
x0 = x==0 ? 0 : x-1
y0 = y==0 ? 0 : y-1
x1 = x
y1 = y
x2 = x+1==@width  ? x : x+1
y2 = y+1==@height ? y : y+1
 
r = g = b = 0.0
[x0, x1, x2].zip(kernel).each do |xx, kcol|
[y0, y1, y2].zip(kcol).each do |yy, k|
r += k * self[xx,yy].r
g += k * self[xx,yy].g
b += k * self[xx,yy].b
end
end
newimg[x,y] = RGBColour.new(luma(r), luma(g), luma(b))
end
 
# Function for clamping values to those that we can use with colors
def luma(value)
if value < 0
0
elsif value > 255
255
else
value
end
end
end
 
 
# Demonstration code using the teapot image from Tk's widget demo
teapot = Pixmap.open('teapot.ppm')
[ ['Emboss', [[-2.0, -1.0, 0.0], [-1.0, 1.0, 1.0], [0.0, 1.0, 2.0]]],
['Sharpen', [[-1.0, -1.0, -1.0], [-1.0, 9.0, -1.0], [-1.0, -1.0, -1.0]]],
['Blur', [[0.1111,0.1111,0.1111],[0.1111,0.1111,0.1111],[0.1111,0.1111,0.1111]]],
].each do |label, kernel|
savefile = 'teapot_' + label.downcase + '.ppm'
teapot.convolute(kernel).save(savefile)
end

[edit] Tcl

Works with: Tcl version 8.6
Library: Tk
package require Tk
 
# Function for clamping values to those that we can use with colors
proc tcl::mathfunc::luma channel {
set channel [expr {round($channel)}]
if {$channel < 0} {
return 0
} elseif {$channel > 255} {
return 255
} else {
return $channel
}
}
# Applies a convolution kernel to produce a single pixel in the destination
proc applyKernel {srcImage x y -- kernel -> dstImage} {
set x0 [expr {$x==0 ? 0 : $x-1}]
set y0 [expr {$y==0 ? 0 : $y-1}]
set x1 $x
set y1 $y
set x2 [expr {$x+1==[image width $srcImage] ? $x : $x+1}]
set y2 [expr {$y+1==[image height $srcImage] ? $y : $y+1}]
 
set r [set g [set b 0.0]]
foreach X [list $x0 $x1 $x2] kcol $kernel {
foreach Y [list $y0 $y1 $y2] k $kcol {
lassign [$srcImage get $X $Y] rPix gPix bPix
set r [expr {$r + $k * $rPix}]
set g [expr {$g + $k * $gPix}]
set b [expr {$b + $k * $bPix}]
}
}
 
$dstImage put [format "#%02x%02x%02x" \
[expr {luma($r)}] [expr {luma($g)}] [expr {luma($b)}]]\
-to $x $y
}
# Apply a convolution kernel to a whole image
proc convolve {srcImage kernel {dstImage ""}} {
if {$dstImage eq ""} {
set dstImage [image create photo]
}
set w [image width $srcImage]
set h [image height $srcImage]
for {set x 0} {$x < $w} {incr x} {
for {set y 0} {$y < $h} {incr y} {
applyKernel $srcImage $x $y -- $kernel -> $dstImage
}
}
return $dstImage
}
 
# Demonstration code using the teapot image from Tk's widget demo
image create photo teapot -file $tk_library/demos/images/teapot.ppm
pack [labelframe .src -text Source] -side left
pack [label .src.l -image teapot]
foreach {label kernel} {
Emboss {
{-2. -1. 0.}
{-1. 1. 1.}
{ 0. 1. 2.}
}
Sharpen {
{-1. -1. -1}
{-1. 9. -1}
{-1. -1. -1}
}
Blur {
{.1111 .1111 .1111}
{.1111 .1111 .1111}
{.1111 .1111 .1111}
}
} {
set name [string tolower $label]
update
pack [labelframe .$name -text $label] -side left
pack [label .$name.l -image [convolve teapot $kernel]]
}
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