Matlab
We can use
imcontour(<image>)
function to compute contour plot of the data in an intensity image. It sets up the axes for orientation and aspect ratio of the image.
a = imread('marbles.jpg'); % reading images
b = rgb2gray(a); % converting image to gray
c = imcontour(b); % finding out contours
figure, imshow(b);
randi() function is used to retrieve an array of integer, valued from give list in order of m*n dimensions used as :
randi([<start range> <ending range>],<number of rows>,<number of columns>)
>> randi([2 6],2,3) % elements 2 to 6 (randomly) in matrix form of 2 rown and 3 columns
ans =
3 5 6
5 3 2
>> randi([1 100],3,4) % elements 1 to 100 (randomly) in matrix form of 3 rown and 4 columns
ans =
67 28 49 55
89 95 14 84
5 87 8 19
fprintf is used to write data to file.
fileID = fopen('anyFile.txt','w'); % OPEN FILE IN WRITE MODE
x = 0:1:10; % RANGE FROM 1 TO 10
A = [x; x.^2]; % x AND SQUARE OF x
fprintf(fileID,'%6s %12s\n','x','Square of x');
fprintf(fileID,'%6.2f %12.8f\n',A);
fclose(fileID);
x Square of x
0.00 0.00000000
1.00 1.00000000
2.00 4.00000000
3.00 9.00000000
4.00 16.00000000
5.00 25.00000000
6.00 36.00000000
7.00 49.00000000
8.00 64.00000000
9.00 81.00000000
10.00 100.00000000
repmat is used for producing repeating matrix in matlab arrays.
repmat( <matrix>, <number of rows> , <number of columns>)
>> repmat([ 1 2 3], 3 , 5)
ans =
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
>> repmat([ 1 2 3], 3 , 2)
ans =
1 2 3 1 2 3
1 2 3 1 2 3
1 2 3 1 2 3
>> a = [2 3; 6 9]
a =
2 3
6 9
>> repmat(a,2,3)
ans =
2 3 2 3 2 3
6 9 6 9 6 9
2 3 2 3 2 3
6 9 6 9 6 9
plot function is used to plot variables in Matlab.
>> y
y =
6 3 5 9 20 42 21 12 55 22
>> x = [ 1 : 10 ]
x =
1 2 3 4 5 6 7 8 9 10
>> y = [6 3 5 9 20 42 21 12 55 22]
y =
6 3 5 9 20 42 21 12 55 22
>> plot(x,y,'.b') % .b for blue dots, you may also remove it, it will work
Determinant tells us values that are useful in systems of linear equations, it also helps us to find inverse of a matrix, also useful in calculus and more, this is calculated as :
(4 * 5) – (8 * 3)
>> a = [4 3; 8 5]
a =
4 3
8 5
>> det(a)
ans =
-4
When there is not enough memory available for extracting all the video frames in matlab, we often read specific frames by giving the indices instead of reading all of the frames into the memory.
obj = mmreader('abc.avi');
vid = read(obj,[150 300]); % read(obj,[<starting index of frames> <ending index of frames>])
frames = obj.NumberOfFrames;
for x = 1 : frames
imwrite(vid(:,:,:,x),strcat('frame-',num2str(x),'.jpeg'));
end
mmreader class is used to read a video object and then this video object is used to read number of frames as obj.NumberOfFrames
obj = mmreader('Megamind.avi');
vid = read(obj);
frames = obj.NumberOfFrames;
for x = 1 : frames
imwrite(vid(:,:,:,x),strcat('frame-',num2str(x),'.jpeg'));
end
figure : figure creates figure graphics objects. Figure objects are the individual windows on the screen in which the MATLAB software displays graphical output.
clear all
a = imread('images111.jpg');
aa = rgb2gray(a);
imshow(aa);
figure, bar3(aa,'r') % r for red bars
imnoise is used to add noise in images.
A = imnoise(I,type) A = imnoise(I,type,parameters) A = imnoise(I,'gaussian',M,V) A = imnoise(I,'localvar',V) A = imnoise(I,'localvar',image_intensity,var) A = imnoise(I,'poisson') A = imnoise(I,'salt & pepper',d) A = imnoise(I,'speckle',v) gpuarrayA = imnoise(gpuarrayI,___)
A = imnoise(I,type) adds noise of a given type to the intensity image I. type is a string that specifies any of the following types of noise. Note that certain types of noise support additional parameters. See the related syntax.
| Value | Description |
|---|---|
| 'gaussian' | Gaussian white noise with constant mean and variance |
| 'localvar' | Zero-mean Gaussian white noise with an intensity-dependent variance |
| 'poisson' | Poisson noise |
| 'salt & pepper' | On and off pixels |
| 'speckle' | Multiplicative noise |
| A = imnoise(I,'gaussian',M,V): adds Gaussian white noise of mean m and variance v to the image I. The default is zero mean noise with 0.01 variance. |
| A = imnoise(I,'localvar',V):adds zero-mean, Gaussian white noise of local variance V to the image I. V is an array of the same size as I. |
| A = imnoise(I,'localvar',image_intensity,var): adds zero-mean, Gaussian noise to an image I, where the local variance of the noise, var, is a function of the image intensity values in I. The image_intensity and var arguments are vectors of the same size, and plot(image_intensity,var) plots the functional relationship between noise variance and image intensity. The image_intensity vector must contain normalized intensity values ranging from 0 to 1. |
| A = imnoise(I,'poisson'): generates Poisson noise from the data instead of adding artificial noise to the data. If I is double precision, then input pixel values are interpreted as means of Poisson distributions scaled up by 1e12. For example, if an input pixel has the value 5.5e-12, then the corresponding output pixel will be generated from a Poisson distribution with mean of 5.5 and then scaled back down by 1e12. If I is single precision, the scale factor used is 1e6. If I is uint8 or uint16, then input pixel values are used directly without scaling. For example, if a pixel in a uint8 input has the value 10, then the corresponding output pixel will be generated from a Poisson distribution with mean 10. |
| A = imnoise(I,'salt & pepper',d): adds salt and pepper noise to the image I, where d is the noise density. This affects approximately d*numel(I) pixels. The default for d is 0.05. |
| A = imnoise(I,'speckle',v): adds multiplicative noise to the image I, using the equation J = I+n*I, where n is uniformly distributed random noise with mean 0 and variance v. The default for v is 0.04. |
| gpuarrayA = imnoise(gpuarrayI,___) :adds noise to the gpuArray intensity image gpuarrayI, performing the operation on a GPU. Returns a gpuArray image J of the same class. |
a = imread('Desert.jpg');
b = imnoise(a,'gaussian',0.01);
subplot(1,2,1); imshow(a); title('a');
subplot(1,2,2); imshow(b); title('b');
double() function Convert symbolic matrix to MATLAB numeric form.
r = double(S)
r = double(S) converts the symbolic object S to a numeric object r.
|
s
|
Symbolic constant, constant expression, or symbolic matrix whose entries are constants or constant expressions. |
|
r
|
If S is a symbolic constant or constant expression, r is a double-precision floating-point number representing the value of S. If S is a symbolic matrix whose entries are constants or constant expressions, r is a matrix of double precision floating-point numbers representing the values of the entries of S. |
>> img = imread('Desert.jpg'); % reading an image
>> class(img) % determining type of image which is object here
ans =
uint8
>> img1 = double(img); % converting object to double
>> class(img1) % determining type of image which is double now
ans =
double
subplot() function is used to plot 1 or more images on the same common MATLAB panel.
subplot(<no of rows>,<no of columns>,<position of element from top left>), <image>;
a = imread('Desert.jpg');
b = imread('desertg.jpg');
c = imread('images.jpg');
d = imread('penguins.jpg');
e = imread('Desertg.jpg');
f = imread('newImage.png');
% MATRIX CONTAINS 2 ROWS AND 3 COLUMNS
% 2x3 IS CONSTANT THROUGHOUT SUBPLOT, ONLY ELEMENT NUMBER CHANGES
subplot(2,3,1), imshow(a); % (first element of 2x3 matrix from top left)
subplot(2,3,2), imshow(b); % (second element of 2x3 matrix from top left)
subplot(2,3,3), imshow(c); % (third element of 2x3 matrix from top left)
subplot(2,3,4), imshow(d); % (fourth element of 2x3 matrix from top left)
subplot(2,3,5), imshow(e); % (fifth element of 2x3 matrix from top left)
subplot(2,3,6), imshow(f); % (sixth element of 2x3 matrix from top left)
The single ampersand & is the logical AND operator. The double ampersand && is again a logical AND operator that employs short-circuiting behaviour. Short-circuiting just means the second operand (right hand side) is evaluated only when the result is not fully determined by the first operand (left hand side).
| A & B | A and B are evaluated |
| A && B | B is only evaluated if A is true |
function y = dummyFunction()
for i = 1:6
if i > 3 && i < 5
disp('less than 5 and greater than 3')
else
disp('greater than 5 and less than 3')
end
end
greater than 5 and less than 3 greater than 5 and less than 3 greater than 5 and less than 3 less than 5 and greater than 3 greater than 5 and less than 3 greater than 5 and less than 3
if expression, statements, end evaluates an expression, and executes a group of statements when the expression is true.
for i = 1:20
if i == 10 % checking if clause
disp('inside 10') % printing message inside 10
break % breaking out from loop
else
disp(i) % printing i value
end
end
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inside 10
The pixel subtraction operator takes two images as input and produces as output a third image whose pixel values are simply those of the first image minus the corresponding pixel values from the second image. It is also often possible to just use a single image as input and subtract a constant value from all the pixels. Some versions of the operator will just output the absolute difference between pixel values, rather than the straightforward signed output.
How It Works :
The subtraction of two images is performed straightforwardly in a single pass. The output pixel values are given by:
Q(i,j) = P1(i,j) – P2(i,j)
Or if the operator computes absolute differences between the two input images then:
Q = |P1(i,j) – P2(i,j)|
Or if it is simply desired to subtract a constant value C from a single image then:
Q = P1(i,j) – C
A = imread('1.jpg');
B = imread('2.jpg');
other1 = imsubtract(B, A); % subtract two images
subplot(1,3,1), imshow(imadd(A,100));
subplot(1,3,2), imshow(imadd(B,100));
subplot(1,3,3), imshow(other1);
Blending Adding images together produces a composite image of both input images. This can be used to produce blending effects using weighted addition.
A = imread('testFirst.jpg');
B = imread('testSecond.jpg');
other1 = imadd(B,A); % added two images
subplot(1,3,1), imshow(imadd(A,100)), title('A'); % added 100 so that image should not look dark
subplot(1,3,2), imshow(imadd(B,100)), title('B'); % added 100 so that image should not look dark
subplot(1,3,3), imshow(other1), title('Final Image'); % displaying final image
