2010_3_Python_Imaging_Library_3.pdf

TechnicalUniversityofŁód¹

InstituteofElectronics

MedicalElectronicsDivision

ImageProcessingandComputer

Graphics

PythonImagingLibrary3

Author:MarekKoci«ski

March2010

1Purpose

To get acquainted with Image ﬁltering using direct access to the pixels. The basic

morphological operations will be applied to binary images.3Dimage will be load into

memory and selected slice will be displayed as2Dimage.

Time

345minutes

2Tasks

1. Open Python interpreter window (Start! Programy! EPD32-6.0.2 ! IDLE)

2. Open new Editor Window (File ! New Window) and write your code into it.

3. Import needed modules, e.g. Image

4. Very often it is needed to get direct access to image data. Function load() returns

a pixel access object that can be used to read and modify pixels. The access object

behaves like a2-dimensional array, so you can do:

pix = im . load ()

print pix [ x , y ]

pix [ x , y ] = value

Create inversion of the brighteness level of the goldhill.bmp image 1.

im = Image . open (" g o l d h i l l .bmp")

im = im . convert ( ‘ ‘L‘ ‘ )

sx = im . s i z e [ 0 ]

sy = im . s i z e [ 1 ]

pix = im . load ()

fory inrange ( sy ) :

forx inrange ( sx ) :

pix [ x , y ] = 255 pix [ x , y ]

im . save ( ’ neg .bmp ’ )

5. Create an function that performs image convolution with33masks. (Hint:

Designing and Implementing Linear Filters in the Spatial Domain) .

(a) Gray-level image

(b) Inverted gray-level image

Figure 1: Pixel operations

def convolution2d (img , k , s i z e =3):

"""This function make image convolutin with cernel .

Kernel as a l i s t from 0 to 8. """

fn=" convolution2d "

print "Function %s" %fn

pix = img . load ()

if s i z e == 3:

print "Kernel s i z e = 3"

sx = img . s i z e [ 0 ]

sy = img . s i z e [ 1 ]

nimg = Image . new (img . mode , img . s i z e )

npix = nimg . load ()

val =[]

fory inrange (1 , sy1):

forx inrange (1 , sx1):

npix [ x , y]= (k [ 0 ] pix [ x1,y1] + k [ 1 ] pix [ x , y1]

+ k [ 2 ] pix [ x+1,y1] + k [ 3 ] pix [ x1,y ]

+ k [ 4 ] pix [ x , y ] + k [ 5 ] pix [ x+1,y ]

+ k [ 6 ] pix [ x1,y+1] + k [ 7 ] pix [ x , y+1]

+ k [ 8 ] pix [ x+1,y+1])

returnnimg

Load image blood1.bmp and perform convolution with Prewitt masks:

Figure 2: Edge detection procedure (from ImageProcessinglectures by P.Strumiłło and

M.Strzelecki)

1 1 1

0 0 0

1 1 1

101

6 4

7 5

6 4

7 5

(a) Prewitt (horiz.) — k1

(b) Prewitt (vertic.) — k3

Pass convolution cernels as the function argument in the following convetnion:

k1=[1;1;1;0;0;0;1;1;1]

k3=[1;0;1;1;0;1;1;0;1]

Find edges of the image using procedure showed in the Fig. 2. Find appropriate

threshold value (Fig. 3).

Add booth resulting images using pixel operations.

a1 = Image . new(im . mode , im . s i z e )

pix_a1_v = a1_v . load ()

pix_a1_h = a1_h . load ()

pix_a1 = a1 . load ()

fory inrange ( sy ) :

forx inrange ( sx ) :

pix_a1 [ x , y ] = (pix_a1_h [ x , y ] + pix_a1_v [ x , y ] )/2

6. Load images bin1.bmp and bin2.bmp. For both images apply function dilation

dilaet2D() and erosion erode2D(). Use dierent structuring element (Fig. 4). Deﬁne

each element as tupe or list, for example ﬁrst element, which is cross shapled is

deﬁned as followes:

se1=(0;1;0;1;1;1;0;1;0;)

Use at least6dierent structuring elements (Fig. 5).

(a) Prewitt (vert.)

(b) Prewitt (horiz.)

(d) After thresholding

Figure 3: Edge detection using Prewitt mask

def dilate2D (im , se , sh =1):

""" dilate2D function i s to do dilation of img image (2D) .

se tuple describes any FULL SIZE se .

se (n) ON piksels

Dilation for WHITE objects with BLACK background ! ! ! """

fn=" dilate2D (im , se )"

if( sh ) :print "Function %s" %fn

sX , sY = im . s i z e

imD = im . load ()

nim = Image . new (im . mode , im . s i z e )

nimD= nim . load ()

seX = 3 #size of se

seY = 3

hx = seX/2

hy = seY/2

fory inrange (hy , sYhy ) :

forx inrange (hx , sXhx ) :

if( imD[ x , y ] == 255 ) :

if( se [ 0 ] ) : nimD[ x1,y1] =255

if( se [ 1 ] ) : nimD[ x , y1] =255

if( se [ 2 ] ) : nimD[ x+1,y1] =255

if( se [ 3 ] ) : nimD[ x1,y ] =255

if( se [ 4 ] ) : nimD[ x , y ] =255

if( se [ 5 ] ) : nimD[ x+1,y ] =255

if( se [ 6 ] ) : nimD[ x1,y+1] =255

if( se [ 7 ] ) : nimD[ x , y+1] =255

if( se [ 8 ] ) : nimD[ x+1,y+1] =255

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