Median filter
You are encouraged to solve this task according to the task description, using any language you may know.
The median filter takes in the neighbourhood the median color (see Median filter)
(to test the function below, you can use these input and output solutions)
Ada
<lang ada>function Median (Picture : Image; Radius : Positive) return Image is
type Extended_Luminance is range 0..10_000_000;
type VRGB is record
Color : Pixel;
Value : Luminance;
end record;
Width : constant Positive := 2*Radius*(Radius+1);
type Window is array (-Width..Width) of VRGB;
Sorted : Window;
Next : Integer;
procedure Put (Color : Pixel) is -- Sort using binary search
pragma Inline (Put);
This : constant Luminance :=
Luminance
( ( 2_126 * Extended_Luminance (Color.R)
+ 7_152 * Extended_Luminance (Color.G)
+ 722 * Extended_Luminance (Color.B)
)
/ 10_000
);
That : Luminance;
Low : Integer := Window'First;
High : Integer := Next - 1;
Middle : Integer := (Low + High) / 2;
begin
while Low <= High loop
That := Sorted (Middle).Value;
if That > This then
High := Middle - 1;
elsif That < This then
Low := Middle + 1;
else
exit;
end if;
Middle := (Low + High) / 2;
end loop;
Sorted (Middle + 1..Next) := Sorted (Middle..Next - 1);
Sorted (Middle) := (Color, This);
Next := Next + 1;
end Put;
Result : Image (Picture'Range (1), Picture'Range (2));
begin
for I in Picture'Range (1) loop
for J in Picture'Range (2) loop
Next := Window'First;
for X in I - Radius .. I + Radius loop
for Y in J - Radius .. J + Radius loop
Put
( Picture
( Integer'Min (Picture'Last (1), Integer'Max (Picture'First (1), X)),
Integer'Min (Picture'Last (2), Integer'Max (Picture'First (2), Y))
) );
end loop;
end loop;
Result (I, J) := Sorted (0).Color;
end loop;
end loop;
return Result;
end Median;</lang> The implementation works with an arbitrary window width. The window is specified by its radius R>0. The resulting width is 2R+1. The filter uses the original pixels of the image from the median of the window sorted according to the luminance. The image edges are extrapolated using the nearest pixel on the border. Sorting uses binary search. (For practical use, note that median filter is extremely slow.)
The following sample code illustrates use: <lang ada> F1, F2 : File_Type; begin
Open (F1, In_File, "city.ppm"); Create (F2, Out_File, "city_median.ppm"); Put_PPM (F2, Median (Get_PPM (F1), 1)); -- Window 3x3 Close (F1); Close (F2);</lang>
BBC BASIC
This example is a 5 x 5 median filter:
<lang bbcbasic> INSTALL @lib$+"SORTLIB"
Sort% = FN_sortinit(0,0)
Width% = 200
Height% = 200
DIM out&(Width%-1, Height%-1)
VDU 23,22,Width%;Height%;8,16,16,128
*DISPLAY Lenagrey
OFF
REM Do the median filtering:
DIM pix&(24)
C% = 25
FOR Y% = 2 TO Height%-3
FOR X% = 2 TO Width%-3
P% = 0
FOR I% = -2 TO 2
FOR J% = -2 TO 2
pix&(P%) = TINT((X%+I%)*2, (Y%+J%)*2) AND &FF
P% += 1
NEXT
NEXT
CALL Sort%, pix&(0)
out&(X%, Y%) = pix&(12)
NEXT
NEXT Y%
REM Display:
GCOL 1
FOR Y% = 0 TO Height%-1
FOR X% = 0 TO Width%-1
COLOUR 1, out&(X%,Y%), out&(X%,Y%), out&(X%,Y%)
LINE X%*2,Y%*2,X%*2,Y%*2
NEXT
NEXT Y%
REPEAT
WAIT 1
UNTIL FALSE</lang>
C
O(n) filter with histogram. <lang c>#include <stdio.h>
- include <stdlib.h>
- include <fcntl.h>
- include <unistd.h>
- include <ctype.h>
- include <string.h>
typedef struct { unsigned char r, g, b; } rgb_t; typedef struct { int w, h; rgb_t **pix; } image_t, *image;
typedef struct { int r[256], g[256], b[256]; int n; } color_histo_t;
int write_ppm(image im, char *fn) { FILE *fp = fopen(fn, "w"); if (!fp) return 0; fprintf(fp, "P6\n%d %d\n255\n", im->w, im->h); fwrite(im->pix[0], 1, sizeof(rgb_t) * im->w * im->h, fp); fclose(fp); return 1; }
image img_new(int w, int h) { int i; image im = malloc(sizeof(image_t) + h * sizeof(rgb_t*) + sizeof(rgb_t) * w * h); im->w = w; im->h = h; im->pix = (rgb_t**)(im + 1); for (im->pix[0] = (rgb_t*)(im->pix + h), i = 1; i < h; i++) im->pix[i] = im->pix[i - 1] + w; return im; }
int read_num(FILE *f) { int n; while (!fscanf(f, "%d ", &n)) { if ((n = fgetc(f)) == '#') { while ((n = fgetc(f)) != '\n') if (n == EOF) break; if (n == '\n') continue; } else return 0; } return n; }
image read_ppm(char *fn) { FILE *fp = fopen(fn, "r"); int w, h, maxval; image im = 0; if (!fp) return 0;
if (fgetc(fp) != 'P' || fgetc(fp) != '6' || !isspace(fgetc(fp))) goto bail;
w = read_num(fp); h = read_num(fp); maxval = read_num(fp); if (!w || !h || !maxval) goto bail;
im = img_new(w, h); fread(im->pix[0], 1, sizeof(rgb_t) * w * h, fp); bail: if (fp) fclose(fp); return im; }
void del_pixels(image im, int row, int col, int size, color_histo_t *h) { int i; rgb_t *pix;
if (col < 0 || col >= im->w) return; for (i = row - size; i <= row + size && i < im->h; i++) { if (i < 0) continue; pix = im->pix[i] + col; h->r[pix->r]--; h->g[pix->g]--; h->b[pix->b]--; h->n--; } }
void add_pixels(image im, int row, int col, int size, color_histo_t *h) { int i; rgb_t *pix;
if (col < 0 || col >= im->w) return; for (i = row - size; i <= row + size && i < im->h; i++) { if (i < 0) continue; pix = im->pix[i] + col; h->r[pix->r]++; h->g[pix->g]++; h->b[pix->b]++; h->n++; } }
void init_histo(image im, int row, int size, color_histo_t*h) { int j;
memset(h, 0, sizeof(color_histo_t));
for (j = 0; j < size && j < im->w; j++) add_pixels(im, row, j, size, h); }
int median(const int *x, int n) { int i; for (n /= 2, i = 0; i < 256 && (n -= x[i]) > 0; i++); return i; }
void median_color(rgb_t *pix, const color_histo_t *h) { pix->r = median(h->r, h->n); pix->g = median(h->g, h->n); pix->b = median(h->b, h->n); }
image median_filter(image in, int size) { int row, col; image out = img_new(in->w, in->h); color_histo_t h;
for (row = 0; row < in->h; row ++) { for (col = 0; col < in->w; col++) { if (!col) init_histo(in, row, size, &h); else { del_pixels(in, row, col - size, size, &h); add_pixels(in, row, col + size, size, &h); } median_color(out->pix[row] + col, &h); } }
return out; }
int main(int c, char **v) { int size; image in, out; if (c <= 3) { printf("Usage: %s size ppm_in ppm_out\n", v[0]); return 0; } size = atoi(v[1]); printf("filter size %d\n", size); if (size < 0) size = 1;
in = read_ppm(v[2]); out = median_filter(in, size); write_ppm(out, v[3]); free(in); free(out);
return 0; }</lang>
D
This uses modules of the Bitmap and Grayscale image Tasks.
The implementation uses algorithm described in Median Filtering in Constant Time paper with some slight differences, that shouldn't have impact on complexity.
Currently this code works only on greyscale images. <lang d>import grayscale_image;
Image!Color medianFilter(uint radius=10, Color)(in Image!Color img) pure nothrow @safe if (radius > 0) in {
assert(img.nx >= radius && img.ny >= radius);
} body {
alias Hist = uint[256];
static ubyte median(uint no)(in ref Hist cumulative)
pure nothrow @safe @nogc {
size_t localSum = 0;
foreach (immutable k, immutable v; cumulative)
if (v) {
localSum += v;
if (localSum > no / 2)
return k;
}
return 0;
}
// Copy image borders in the result image.
auto result = new Image!Color(img.nx, img.ny);
foreach (immutable y; 0 .. img.ny)
foreach (immutable x; 0 .. img.nx)
if (x < radius || x > img.nx - radius - 1 ||
y < radius || y > img.ny - radius - 1)
result[x, y] = img[x, y];
enum edge = 2 * radius + 1; auto hCol = new Hist[img.nx];
// Create histogram columns.
foreach (immutable y; 0 .. edge - 1)
foreach (immutable x, ref hx; hCol)
hx[img[x, y]]++;
foreach (immutable y; radius .. img.ny - radius) {
// Add to each histogram column lower pixel.
foreach (immutable x, ref hx; hCol)
hx[img[x, y + radius]]++;
// Calculate main Histogram using first edge-1 columns.
Hist H;
foreach (immutable x; 0 .. edge - 1)
foreach (immutable k, immutable v; hCol[x])
if (v)
H[k] += v;
foreach (immutable x; radius .. img.nx - radius) {
// Add right-most column.
foreach (immutable k, immutable v; hCol[x + radius])
if (v)
H[k] += v;
result[x, y] = Color(median!(edge ^^ 2)(H));
// Drop left-most column.
foreach (immutable k, immutable v; hCol[x - radius])
if (v)
H[k] -= v;
}
// Substract the upper pixels.
foreach (immutable x, ref hx; hCol)
hx[img[x, y - radius]]--;
}
return result;
}
version (median_filter_main)
void main() { // Demo.
loadPGM!Gray(null, "lena.pgm").
medianFilter!10
.savePGM("lena_median_r10.pgm");
}</lang>
Compile with -version=median_filter_main to run the demo.
GDL
GDL has no inbuilt median filter function, which is native in IDL. This example is based on pseudocode here: http://en.wikipedia.org/wiki/Median_filter#2D_median_filter_pseudo_code, however, it works with 1D arrays only. It does not process boundaries. <lang GDL> FUNCTION MEDIANF,ARRAY,WINDOW RET=fltarr(N_ELEMENTS(ARRAY),1) EDGEX=WINDOW/2 FOR X=EDGEX, N_ELEMENTS(ARRAY)-EDGEX DO BEGIN PRINT, "X", X COLARRAY=fltarr(WINDOW,1) FOR FX=0, WINDOW-1 DO BEGIN COLARRAY[FX]=ARRAY[X + FX - EDGEX] END T=COLARRAY[SORT(COLARRAY)] RET[X]=T[WINDOW/2] END RETURN, RET END </lang> Usage: <lang GDL>Result = MEDIANF(ARRAY, WINDOW)</lang>
Go
Implemented with existing GetPx/SetPx functions at Grayscale image task. It could be sped up by putting code in the raster package, but if you're concerned about speed, you should implement one of the O(n) algorithms available. <lang go>package main
// Files required to build supporting package raster are found in: // * Bitmap // * Grayscale image // * Read a PPM file // * Write a PPM file
import (
"fmt" "raster"
)
var g0, g1 *raster.Grmap var ko [][]int var kc []uint16 var mid int
func init() {
// hard code box of 9 pixels
ko = [][]int{
{-1, -1}, {0, -1}, {1, -1},
{-1, 0}, {0, 0}, {1, 0},
{-1, 1}, {0, 1}, {1, 1}}
kc = make([]uint16, len(ko))
mid = len(ko) / 2
}
func main() {
// Example file used here is Lenna50.jpg from the task "Percentage
// difference between images" converted with with the command
// convert Lenna50.jpg -colorspace gray Lenna50.ppm
// It shows very obvious compression artifacts when viewed at higher
// zoom factors.
b, err := raster.ReadPpmFile("Lenna50.ppm")
if err != nil {
fmt.Println(err)
return
}
g0 = b.Grmap()
w, h := g0.Extent()
g1 = raster.NewGrmap(w, h)
for y := 0; y < h; y++ {
for x := 0; x < w; x++ {
g1.SetPx(x, y, median(x, y))
}
}
// side by side comparison with input file shows compression artifacts
// greatly smoothed over, although at some loss of contrast.
err = g1.Bitmap().WritePpmFile("median.ppm")
if err != nil {
fmt.Println(err)
}
}
func median(x, y int) uint16 {
var n int
// construct sorted list as pixels are read. insertion sort can't be
// beat for a small number of items, plus there would be lots of overhead
// just to get numbers in and out of a library sort routine.
for _, o := range ko {
// read a pixel of the kernel
c, ok := g0.GetPx(x+o[0], y+o[1])
if !ok {
continue
}
// insert it in sorted order
var i int
for ; i < n; i++ {
if c < kc[i] {
for j := n; j > i; j-- {
kc[j] = kc[j-1]
}
break
}
}
kc[i] = c
n++
}
// compute median from sorted list
switch {
case n == len(kc): // the usual case, pixel with complete neighborhood
return kc[mid]
case n%2 == 1: // edge case, odd number of pixels
return kc[n/2]
}
// else edge case, even number of pixels
m := n / 2
return (kc[m-1] + kc[m]) / 2
}</lang>
J
The task could be solved the following way. First, for each pixel of input, collect pixels which fall into the corresponding window, where median value will be calculated. Then, for each window - the set of pixels - find the median value. To compare 3-channel pixels we first convert them into 1-channel gray values.
The following verbs are used to work with bitmaps:
<lang J> makeRGB=: 0&$: : (($,)~ ,&3) toGray=: <. @: (+/) @: (0.2126 0.7152 0.0722 & *)"1 </lang>
We'll determine the window as a square zone around each pixel, with the given pixel in the center of the zone. Such a window always have odd height and width. We'll say the window radius is 0 if the window contain only the given pixel - in this case the resulting picture will be identical to the input. The radius is 1 if the window is 3x3 pixels, with given pixel in the center. Radius is 2 if the window is 5x5 pixels, with given pixel in the center, etc.
To get all pixels in the window, first calculate coordinates - or indexes - of those pixels. For the pixels on the edges of the input bitmap, include only those indexes which correspond to actually existing pixels - no negative indexes and no indexes outside of the bitmap boundaries.
<lang J> median_filter =: dyad define
win =. y -~ i. >: +: y
height =. {: }: $ x
width =. {. }: $ x
h_indexes =. < @ (#~ >:&0 * <&height) @ (win&+)"0 i. height
w_indexes =. < @ (#~ >:&0 * <&width) @ (win&+)"0 i. width
sets =. w_indexes < @ ({&x) @ < @ ,"0 0/ h_indexes
medians =. ({~ <. @ -: @ {. @ $) @ ({~ /: @: toGray) @ (,/) @ > sets
) </lang>
Example: <lang J>
] bmp =. ?. 256 + makeRGB 4 5 34 39 168
133 133 40 210 137 244
66 183 114
211 241 75
212 68 13
91 246 128
203 236 213 162 92 165
90 203 161
104 124 113 199 61 60 135 179 241 142 156 125
64 77 61
130 70 200 114 32 55
94 211 182 29 49 252
116 139 217
bmp median_filter 1
133 133 40 210 137 244 210 137 244
90 203 161 90 203 161
104 124 113 133 133 40
66 183 114
210 137 244
66 183 114
212 68 13 104 124 113 142 156 125 142 156 125 116 139 217
130 70 200 104 124 113 142 156 125 142 156 125 116 139 217 </lang>
Julia
<lang julia>using Images, ImageFiltering, FileIO Base.isless(a::RGB{T}, b::RGB{T}) where T =
red(a) < red(b) || green(a) < green(b) || blue(a) < blue(b)
Base.middle(x::RGB) = x
img = load("data/lenna100.jpg") mapwindow(median!, img, (3, 3))</lang>
Kotlin
We reuse the class in the Bitmap task for this and add a member function to filter the image as per the Wikipedia pseudo-code. The colors in the Window array are sorted by their luminance.
To test the function we use the left half of the sample image file (Medianfilterp.png) in the Wikipedia article and see if we can get close to the right half. <lang scala>// Version 1.2.41 import java.awt.Color import java.awt.Graphics import java.awt.image.BufferedImage import java.io.File import javax.imageio.ImageIO
class BasicBitmapStorage(width: Int, height: Int) {
val image = BufferedImage(width, height, BufferedImage.TYPE_3BYTE_BGR)
fun fill(c: Color) {
val g = image.graphics
g.color = c
g.fillRect(0, 0, image.width, image.height)
}
fun setPixel(x: Int, y: Int, c: Color) = image.setRGB(x, y, c.getRGB())
fun getPixel(x: Int, y: Int) = Color(image.getRGB(x, y))
fun medianFilter(windowWidth: Int, windowHeight: Int) {
val window = Array(windowWidth * windowHeight) { Color.black }
val edgeX = windowWidth / 2
val edgeY = windowHeight / 2
val compareByLuminance = {
c: Color -> 0.2126 * c.red + 0.7152 * c.green + 0.0722 * c.blue
}
for (x in edgeX until image.width - edgeX) {
for (y in edgeY until image.height - edgeY) {
var i = 0
for (fx in 0 until windowWidth) {
for (fy in 0 until windowHeight) {
window[i] = getPixel(x + fx - edgeX, y + fy - edgeY)
i++
}
}
window.sortBy(compareByLuminance)
setPixel(x, y, window[windowWidth * windowHeight / 2])
}
}
}
}
fun main(args: Array<String>) {
val img = ImageIO.read(File("Medianfilterp.png"))
val bbs = BasicBitmapStorage(img.width / 2, img.height)
with (bbs) {
for (y in 0 until img.height) {
for (x in 0 until img.width / 2) {
setPixel(x, y, Color(img.getRGB(x, y)))
}
}
medianFilter(3, 3)
val mfFile = File("Medianfilterp2.png")
ImageIO.write(image, "png", mfFile)
}
}</lang>
- Output:
Similar to right-half of Wikipedia image - color definition and brightness seem better but remaining distortion more evident.
Mathematica
<lang Mathematica> MedianFilter[img,n] </lang>
OCaml
<lang ocaml>let color_add (r1,g1,b1) (r2,g2,b2) =
( (r1 + r2), (g1 + g2), (b1 + b2) )
let color_div (r,g,b) d =
( (r / d), (g / d), (b / d) )
let compare_as_grayscale (r1,g1,b1) (r2,g2,b2) =
let v1 = (2_126 * r1 + 7_152 * g1 + 722 * b1) and v2 = (2_126 * r2 + 7_152 * g2 + 722 * b2) in (Pervasives.compare v1 v2)
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 median_value img radius =
let samples = (radius*2+1) * (radius*2+1) in fun x y -> let sample = ref [] in
for _x = (x - radius) to (x + radius) do
for _y = (y - radius) to (y + radius) do
let v = get_rgb img _x _y in
sample := v :: !sample;
done;
done;
let ssample = List.sort compare_as_grayscale !sample in let mid = (samples / 2) in
if (samples mod 2) = 1
then List.nth ssample (mid+1)
else
let median1 = List.nth ssample (mid)
and median2 = List.nth ssample (mid+1) in
(color_div (color_add median1 median2) 2)
let median img radius =
let _, r_channel,_,_ = img in let width = Bigarray.Array2.dim1 r_channel and height = Bigarray.Array2.dim2 r_channel in
let _median_value = median_value img radius in
let res = new_img ~width ~height in
for y = 0 to pred height do
for x = 0 to pred width do
let color = _median_value x y in
put_pixel res color x y;
done;
done;
(res)</lang>
an alternate version of the function median_value using arrays instead of lists: <lang ocaml>let median_value img radius =
let samples = (radius*2+1) * (radius*2+1) in
let sample = Array.make samples (0,0,0) in
fun x y ->
let i = ref 0 in
for _x = (x - radius) to (x + radius) do
for _y = (y - radius) to (y + radius) do
let v = get_rgb img _x _y in
sample.(!i) <- v;
incr i;
done;
done;
Array.sort compare_as_grayscale sample; let mid = (samples / 2) in
if (samples mod 2) = 1
then sample.(mid+1)
else (color_div (color_add sample.(mid)
sample.(mid+1)) 2)</lang>
Perl 6
<lang perl6># Reference: https://github.com/azawawi/perl6-magickwand
- WIP by module author
- !/usr/bin/env perl6
use v6;
use MagickWand;
- A new magic wand
my $original = MagickWand.new;
- Read an image
$original.read("image/example.jpg");
- apply median filter
say "Median Filter..."; my $o = $original.clone; $o.median-filter; $o.label("Median Filter");
- And then write a new image
$o.write("output.png");
- And cleanup on exit
LEAVE {
$original.cleanup if $original.defined; $o.cleanup if $o.defined;
}</lang>
Phix
Requires read_ppm() from Read_a_PPM_file, write_ppm() from Write_a_PPM_file, which are both now part of demo\rosetta\ppm.e. Results may be verified with demo\rosetta\viewppm.exw <lang Phix>-- demo\rosetta\Bitmap_Median_filter.exw include ppm.e
constant neigh = {{-1,-1},{0,-1},{1,-1},
{-1, 0},{0, 0},{1, 0},
{-1, 1},{0, 1},{1, 1}}
--constant neigh = {{-2,-2},{-1,-2},{0,-2},{1,-2},{2,-2}, -- {-2,-1},{-1,-1},{0,-1},{1,-1},{2,-1}, -- {-2, 0},{-1, 0},{0, 0},{1, 0},{2, 0}, -- {-2, 1},{-1, 1},{0, 1},{1, 1},{2, 1}, -- {-2, 2},{-1, 2},{0, 2},{1, 2},{2, 2}}
sequence kn = repeat(0,length(neigh))
function median(sequence image)
integer h = length(image),
w = length(image[1])
for i=1 to length(image) do
for j=1 to length(image[i]) do
integer n = 0, c, p, x, y
for k=1 to length(neigh) do
x = i+neigh[k][1]
y = j+neigh[k][2]
if x>=1 and x<=h
and y>=1 and y<=w then
n += 1
c = image[x,j]
p = n
while p>1 do
if c>kn[p-1] then exit end if
kn[p] = kn[p-1]
p -= 1
end while
kn[p] = c
end if
end for
if and_bits(n,1) then
c = kn[(n+1)/2]
else
c = floor((kn[n/2]+kn[n/2+1])/2)
end if
image[i,j] = c
end for
end for
return image
end function
sequence img = read_ppm("Lena.ppm")
img = median(img)
write_ppm("LenaMedian.ppm",img)</lang>
PicoLisp
<lang PicoLisp>(de ppmMedianFilter (Radius Ppm)
(let Len (inc (* 2 Radius))
(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
(cdr
(get
(sort
(mapcan
'((Y)
(mapcar
'((C)
(cons
(+
(* (car C) 2126) # Red
(* (cadr C) 7152) # Green
(* (caddr C) 722) ) # Blue
C ) )
(head Len Y) ) )
X ) )
(inc Radius) ) ) )
(map pop X) ) ) ) ) ) ) )</lang>
Test using 'ppmRead' from Bitmap/Read a PPM file#PicoLisp and 'ppmWrite' from Bitmap/Write a PPM file#PicoLisp:
(ppmWrite (ppmMedianFilter 2 (ppmRead "Lenna100.ppm")) "a.ppm")
Python
<lang python>import Image, ImageFilter im = Image.open('image.ppm')
median = im.filter(ImageFilter.MedianFilter(3)) median.save('image2.ppm')</lang>
Racket
Due to the use of flomaps the solution below works for all types of images. <lang racket>
- lang racket
(require images/flomap math)
(define lena <<paste image of Lena here>> ) (define bm (send lena get-bitmap)) (define fm (bitmap->flomap bm))
(flomap->bitmap
(build-flomap
4 (send bm get-width) (send bm get-height)
(λ (k x y)
(define (f x y) (flomap-ref fm k x y))
(median < (list (f (- x 1) (- y 1))
(f (- x 1) y)
(f (- x 1) (+ y 1))
(f x (- y 1))
(f x y)
(f x (+ y 1))
(f (+ x 1) (- y 1))
(f (+ x 1) y)
(f (+ x 1) (+ y 1)))))))
</lang>
Ruby
<lang ruby>class Pixmap
def median_filter(radius=3)
radius += 1 if radius.even?
filtered = self.class.new(@width, @height)
pb = ProgressBar.new(@height) if $DEBUG
@height.times do |y|
@width.times do |x|
window = []
(x - radius).upto(x + radius).each do |win_x|
(y - radius).upto(y + radius).each do |win_y|
win_x = 0 if win_x < 0
win_y = 0 if win_y < 0
win_x = @width-1 if win_x >= @width
win_y = @height-1 if win_y >= @height
window << self[win_x, win_y]
end
end
# median
filtered[x, y] = window.sort[window.length / 2]
end
pb.update(y) if $DEBUG
end
pb.close if $DEBUG
filtered
end
end
class RGBColour
# refactoring def luminosity Integer(0.2126*@red + 0.7152*@green + 0.0722*@blue) end def to_grayscale l = luminosity self.class.new(l, l, l) end
# defines how to compare (and hence, sort) def <=>(other) self.luminosity <=> other.luminosity end
end
class ProgressBar
def initialize(max)
$stdout.sync = true
@progress_max = max
@progress_pos = 0
@progress_view = 68
$stdout.print "[#{'-'*@progress_view}]\r["
end
def update(n)
new_pos = n * @progress_view/@progress_max
if new_pos > @progress_pos
@progress_pos = new_pos
$stdout.print '='
end
end
def close $stdout.puts '=]' end
end
bitmap = Pixmap.open('file') filtered = bitmap.median_filter</lang>
Tcl
<lang tcl>package require Tk
- Set the color of a pixel
proc applyMedian {srcImage x y -> dstImage} {
set x0 [expr {$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 {}]]
foreach X [list $x0 $x1 $x2] {
foreach Y [list $y0 $y1 $y2] { lassign [$srcImage get $X $Y] rPix gPix bPix lappend r $rPix lappend g $gPix lappend b $bPix }
} set r [lindex [lsort -integer $r] 4] set g [lindex [lsort -integer $g] 4] set b [lindex [lsort -integer $b] 4] $dstImage put [format "#%02x%02x%02x" $r $g $b] -to $x $y
}
- Apply the filter to the whole image
proc medianFilter {srcImage {dstImage ""}} {
if {$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} { applyMedian $srcImage $x $y -> $dstImage }
} return $dstImage
}
- Demonstration code using the Tk widget demo's teapot image
image create photo teapot -file $tk_library/demos/images/teapot.ppm pack [labelframe .src -text Source] -side left pack [label .src.l -image teapot] update pack [labelframe .dst -text Median] -side left pack [label .dst.l -image [medianFilter teapot]]</lang>
zkl
Uses Image Magick and the PPM class from http://rosettacode.org/wiki/Bitmap/Bresenham%27s_line_algorithm#zkl
<lang zkl>fcn medianFilter(img){ //-->new image
var [const] window=[-2..2].walk(), edge=(window.len()/2); // 5x5 window
MX,MY,new := img.w,img.h,PPM(MX,MY);
pixel,pixels:=List(),List();
foreach x,y in ([edge..MX-edge-1],[edge..MY-edge-1]){
pixels.clear();
foreach ox,oy in (window,window){ // construct sorted list as pixels are read.
pixels.merge(pixel.clear(img[x+ox, y+oy])); // merge sort two lists
}
new[x,y]=pixels[4]; // median value
}
new
}</lang> <lang zkl>filtered:=medianFilter(PPM.readJPGFile("lena.jpg")); filtered.writeJPGFile("lenaMedianFiltered.zkl.jpg");</lang> See the filtered image and the orginal.
