Brief Matlab tutorial

Transcription

1 Basic data operations: Brief Matlab tutorial Basic arithmetic operations: >> 2+7 ans = 9 All basic arithmetic operations covered (+, -, *, ^). Vectors and matrices Vectors: x = [ ] x = Generate a string of ones a = ones(1, 5) a= a vector row of ones with a length of 5 elements Generate a string of zeros b = zeros(1,5) b = Problem: how to generate a column vector of ones with a length of 5 elements Two possibilities: a. C = ones(5,1) b. C = ones(1,5) -> transpose operation i.e. c = a

2 Access the value of an element at the specified position >>X = [ ] >>a = X(1) a = 1 >>b = X(2) b = -2 How to access multiple positions: >>Y = [ ] >>Z = Y(1:4) Z = Max/Min operations >>max(x) 11 >>min(x) -2 See also: sort, mean, std How to retrieve the size of the vector >>[m,n] = size(x) m = 1 n = 7 Common operations: A = [1 2 3] B = [3 2 1] C = A+B = [4 4 4] C = A-B = [-2 0 2] Is it possible to perform A*B? But A*B?

3 How to perform element-wise vector multiplication C = A.* B = [3 4 3] Other operations: Generate a linear vector: >>X = linspace(1,4,4) // start, final, no_points_generated X = >>X = linspace(1,4,8) X = >>Y = logspace(1, 4, 4) Y = Plot the value of the vector into a graph: >>x = [ ] >> plot(x)

4 Matrices: >> X = [1 2 3 ; 3 2 1; ] X = Generate a matrix of ones >> Y = ones (4, 5) Y = Generate a matrix of zeros >>Y = zeros (4, 5) Y = Standard operations: Transpose, inverse >>C = [1 2 3; 3 2 1; ] C = >>B = C B =

5 >> D = inv(c) Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND = e-018. D = 1.0e+016 * why? >> det(c) -> ans: 0 Standard operations for matrix arithmetic covered. Addition, subtraction, multiplication with a constant, multiplication, element-wise multiplication Example: Matrix multiplication: Ensure that the dimensions of the matrices agree to carry out the operation successfully. >>A = [ ; ] >>B = [2 3; 3 4; 3 1; 2 2] >> C = A*B C = Element wise multiplication >>A = [1 2 3; 2 3 1] >>B = [2 3 1; ] >> C = A.*B C =

6 Getting the size of the matrix >>A = [1 2 3; 2 3 1] >>[m,n] = size(a) m = 2 n = 3 Access the data within the matrix: >>A= [1 2 3; 3 4 5; 4 3 2] A = >>c = A (2, 2) c = 4 >>d = A(2:3, 2:3) d = A = plot(a)

7 Loops >> for i=1:5 q=i+1 q =2 q =3 q =4 q =5 q =6 >> A = [1 2 3; ; 3 2 1] A = for i =1:3 for j= 1:3 A(i,j) = A(i,j)^3; >> A A =

8 >> a =1; >> n = 20; while 2^a <n a= a+1 a = 2 a =3 a =4 a =5 Relational operators (<, <=, >, >=, = =, ~=) Note that for matlab versions >=6 == equivalent with = Logical operators: & AND OR ~ NOT If-else statement if relation Statements elseif relation Statements else Statements Logical operators (&,, ~) can be used to create various relations.

9 x = -1:0.05:1; //generates an array from 1 to 1 in steps of 0.05 F = zeros(size(x)); //output [m,n] = size(x); for i=1:n if x(i)>0 F(i)=0; else F(i)= 1; x = Columns 1 through Columns 10 through Columns 19 through Columns 28 through Columns 37 through F= Columns 1 through Columns 16 through Columns 31 through break statement for i=1:n if x(i)>0 break else x(i) ans =

10 Examples VSG toolbox % Convolution example % read in the image in_img1=openimage('images\texteg.bmp'); % Define the N-tuple to be used conv_arr=zeros(11,11); conv_arr(2:10,4:5)=1; conv_arr(4,2:9)=1; conv_arr(11,5:9)=1; % Convert to white text on a black background [out_img1]=vsg('midthresh',in_img1); [out_img2]=vsg('not',out_img1); % Convolve the text image with the N-tuple [out_img3]=vsg('convolution',out_img2,conv_arr); % Threshold the result of the convolution. A high output from the % convolution indicates that the N-tuple is very similar to the region in % the input image at the location of the high response. thresh=255; [out_img4]=vsg('threshold',out_img3,thresh); % Draw a square at each location that the response of the convolution was % greater than the threshold, and display the squares on the white text on % black background image. [out_img5]=vsg('point2square',out_img4); greyvalue=128; [out_img6]=vsg('bingrey',out_img2,greyvalue); [out_img7]=vsg('add',out_img5,out_img6); % Display the images h=figure; image(uint8(in_img1)); colormap(gray); set(h,'name','input');axis image; h=figure; imagesc(uint8(conv_arr)); colormap(gray); set(h,'name','convolution Kernel');axis image; h=figure; image(uint8(out_img4)); colormap(gray); set(h,'name','letter Locations');axis image; h=figure; image(uint8(out_img7)); colormap(gray); set(h,'name','highlighted text');axis image;

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12 % Largest size object identification % i.e. get the largest component from the binary image %open the image in_img1 = openimage('images\adqu.gif'); in_img1 = in_img1(:,:,1); %if color image, extract only the red (R) component h=figure; imagesc(uint8(in_img1)); colormap(gray); set(h,'name','input image'); %ensure that the data is in binary form [out_img1] = vsg ('Threshold', in_img1, 127); %mask the data for labelling operation [out_img2]= vsg ('MaskImg',out_img1,3); %apply labelling [out_img3] = vsg ('Labeller', out_img2); %number of components in the labelled image - largest grayscale value numcomp = vsg ('HighestGrey',out_img3); %generate the output image [m,n] = size(in_img1); [outimage] = zeros(m,n); [pix] = zeros(1,numcomp); %holds the region size for each label for i=1:numcomp [imgtemp] = vsg ('DualThresh',out_img3,i,i); string = ['Object label ' num2str(i)]; h=figure; imagesc(uint8(imgtemp)); colormap(gray); set(h,'name',string); pix(i) = vsg ('WPCounter',imgTemp); ; [area, label] = max(pix); string=['the label of the largest region is: ' num2str(label)]; disp(string); string=['the area of the largest region is: ' num2str(area)]; disp(string); [outimage] = vsg ('DualThresh',out_img3,label,label); h=figure; imagesc(uint8(outimage)); colormap(gray); set(h,'name','largest labelled object'); [reghighlighted] = vsg ('Add', outimage, in_img1); h=figure; imagesc(uint8(reghighlighted)); colormap(gray); set(h,'name','region Highlighted'); Question: Is there any alternative and simpler solution to extract the largest character in the input image?

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14 Results: