Exploring the following MATLAB Commands* used to play with the images:
- imread
- imwrite
- rgb2gray
- imshow
- imresize
- imrotate
- imhist
- histeq
- improfile
- grayslice
- phantom
- colormap
- imcomplement
- imadd
- imdivide
- immultiply
- imsubtract
- im2bw
- im2double
- mat2gray
- imtransform
- imadjust
- maketform
- entropy
- rangefilt
- stdfilt
- corr2
- conv2
- impixel
- mean2
- std2
- imnoise
- fspecial
- imfilter
- freqz2
- dct2
- idct2
- fft2
- ifft2
- Apart from the above useful commands, explore the Image Processing Toolbox from the MATLAB Help and go through the Examples and Demos for understanding the capabilities of the Image Processing Toolbox.
So to learn basic commands associated with image processing in MATLAB try these simple basic programs to see the effect on images by applying them.
Open your MATLAB then open editor and try the program below save it and run the program, add an image to your Matlab directory by simply drag and drop method into Matlab command window.
close all;
clear all;
clc;
a = imread('new.png'); % imread
function
display('size of original image, stored in a')
size(a)
imwrite(a,'Arjun.jpg','jpg'); % imwrite function
subplot(2,2,1)
imshow(a) % imshow function
title('imread stores image
values in a ')
b =
imresize(a,[64,64]); % imresize function
display('size of resized image to a new scale, stored in b')
size(b)
subplot(2,2,2)
imshow(b) % imshow function
title('imresize resizes image
to a new scale values, stored in b ')
c =
imrotate(a,30); % imrotate function
display('size of rotated image at some angle, stored in c')
size(c)
subplot(2,2,3)
imshow(c)
title('imrotate rotates image
at given angle, stored in c ')
d =
rgb2gray(a); % rgb2gray function
size(d)
display('size of grayscale image converted from original image, stored
in d')
subplot(2,2,4)
imshow(d)
title('rgb2gray converts rgb
image in grayscale image, stored in d ')
close
all;
clear
all;
clc;
a
= imread('new.png'); % imread function
display('size of original image, stored in a')
size(a)
d
= rgb2gray(a); % rgb2gray function
display('size of grayscale image converted from original
image, stored in d')
size(d)
subplot(2,2,1)
imshow(d)
title('rgb2gray
converts rgb image in grayscale image, stored in d ')
subplot(2,2,3)
imhist(d);
title('imhist
displays a histogram for the grayscale image ')
xlabel('number of gray levels')
ylabel('probability of occurance')
f
= histeq(d);
subplot(2,2,2)
imshow(f)
title('histeq displays an equalization of a histogram
for the grayscale image ')
subplot(2,2,4)
imhist(f);
xlabel('number of gray levels')
ylabel('probability of occurance')
title('imhist
displays a histogram for the grayscale image ')
close
all;
clear
all;
clc;
a
= imread('new.png'); % imread function
d
= rgb2gray(a);
display('size of original image, stored in a')
size(a)
subplot(2,2,1)
imshow(a)
title('rgb2gray
converts rgb image in grayscale image, stored in d ')
x
= [19 427 416 77];
y
= [96 462 37 33];
subplot(2,2,3)
improfile(a,x,y),grid
on;
title('improfile computes the intensity values along a
line or a multiline path in an image')
subplot(2,2,2)
grayslice(a,64)
title('grayslice thresholds the intensity image')
p
= phantom('Modified
Shepp-Logan',250);
subplot(2,2,4)
imshow(p)
title('phantom function')
close
all;
clear
all;
clc;
a
= imread('new.png'); % imread function
display('size of original image, stored in a')
size(a)
subplot(2,2,1)
load
flujet
image(a)
colormap(jet)
h
= imcomplement(a);
title('colormap function')
subplot(2,2,2)
imshow(h)
title('imcomplement
computes complement of an image, stored in h ')
i
= imadd(a,200);
subplot(2,2,3)
imshow(i)
title('imadd add
the scalar value to the image, stored in
i ')
i
= imdivide(a,0.2);
subplot(2,2,4)
imshow(i)
title('imdivide divides the image values by given ,
stored in h ')
close
all;
clear
all;
clc;
a
= imread('new.png'); % imread function
display('size of original image, stored in a')
size(a)
j
= immultiply(a,20);
subplot(2,2,1)
imshow(j)
title('immultiply
multiplies the given scalar to the image values, stored in j')
k
= imsubtract(a,60);
subplot(2,2,2)
imshow(k)
title('imsubtract
substract the given scalar from the image values, stored in k')
l
= im2bw(a,0.2);
subplot(2,2,3)
imshow(l)
title('im2bw converts the grayscale image to a binary
image, stored in l')
m
= im2double(a);
subplot(2,2,4)
imshow(m)
title('im2bw converts the intensity image to double
precision, stored in m')
close
all;
clear
all;
clc;
a
= imread('new.png'); % imread function
display('size of original image, stored in a')
size(a)
m
= mat2gray(a,[0.8 0.1]);
subplot(2,2,1)
imshow(m)
title('mat2gray
converts the matrix of image to the intensity image, stored in m')
tform
= maketform('a',[1 0 0; .5 1 0; 0 0 1]);
n
= imtransform(a,tform);
subplot(2,2,2)
imshow(n)
title('imtransform
ransforms the image A according to the 2-D spatial transformation,
stored in n')
o
= imadjust(a,[0.1;0.9],[0.2;0.9]);
subplot(2,2,3)
imshow(o)
title('Adjust image intensity values or colormap ,
stored in o')
T
= maketform('affine',[.5 0 0; .5 2 0; 0 0 1]);
tformfwd([10
20],T)
p
= imtransform(a,T);
subplot(2,2,4)
imshow(p)
title('Creates spatial transformation structure (TFORM)
, stored in p')
close all;
clear all;
clc;
a = imread('Audi A9_214.jpg');
q = entropy(a)
r = rangefilt(a)
A = rand(3)
B = rand(4)
D = rand(3)
display('convolution of A and B')
C = conv2(A,B)
display('correlation of A and D')
c = corr2(A,D)
b = stdfilt(a);
display('displaying value of pixel which was clicked on an output image')
d = impixel(a)
display('computes mean value for image a')
m = mean2(a)
display('computes standard variance for image a')
n = std2(a)
o = imnoise(a)
size(a)
subplot(2,2,1)
imshow(a)
title('original image')
subplot(2,2,2)
imshow(r)
title('image after using function
rangefilt')
subplot(2,2,3)
imshow(b)
title('image after using function
stdfilt')
subplot(2,2,4)
imshow(o)
title('image after using function
imnoise')
Output –
A =
0.3773 0.1774 0.3131
0.7743 0.3386 0.2156
0.0057 0.5241 0.2778
B =
0.0954 0.9668 0.9908
0.3074
0.9177 0.9252 0.7247
0.7215
0.2934 0.8329 0.4984
0.9551
0.0127 0.3989 0.5377
0.2296
D =
0.1657 0.6841 0.9552
0.9906 0.3217 0.2197
0.5798 0.5987 0.1407
convolution of A and B
C =
0.0360 0.3817 0.5752
0.5945 0.3648 0.0963
0.4202 1.2928 1.8401
1.4725 0.6726 0.2922
0.8218 1.4490 2.0389
2.5028 1.1624 0.5400
0.2372 1.3833 1.7528
2.0358 1.2195 0.4783
0.0115 0.4717 1.0751
0.9440 0.8327 0.3148
0.0001 0.0090 0.2157
0.3939 0.2697 0.0638
correlation of A and D computes
mean value for image a computes
standard variance for image a
c = 0.3665 m
= 130.5352 n = 91.3458
ans = 800
960 3
close
all;
clear
all;
clc;
a
= imread('Avengers_230.jpg'); % imread function
display('size of original image, stored in a')
size(a)
subplot(2,2,1)
imshow(a)
title('original image')
b
= fspecial('motion',20,45);
c
= imfilter(a,b,'replicate');
subplot(2,2,2);
imshow(c);title('Motion Blurred Image');
d
= fspecial('disk',10);
e
= imfilter(a,d,'replicate');
subplot(2,2,3);
imshow(e);
title('Blurred Image');
f
= fspecial('unsharp');
g
= imfilter(a,f,'replicate');
subplot(2,2,4);
imshow(g);
title('Sharpened Image');
close
all;
clear
all;
clc;
a
= imread('new.png'); % imread function
display('size of original image, stored in a')
d
= rgb2gray(a);
size(a)
b
= dct2(d);
subplot(2,2,1)
imshow(b)
title('dct of given image using function dct2')
b(abs(b)
< 10) = 0;
K
= idct2(b);
subplot(2,2,2)
imshow(K,[0
255])
title('reconstruct the image using the inverse DCT
function idct2')
f
= fft2(a);
subplot(2,2,3)
imshow(f)
title('fft of given image using function fft2')
g
= ifft2(f);
subplot(2,2,4)
imshow(g)
title('inverse fft of given image using function ifft2')
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