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Lab of COMP 319 Image Processing with MATLAB Lab tutor : Shenghua ZHONG Email: zsh696@gmail.com csshzhong@comp.polyu.edu.hk Lab 2: Nov 9, 2011 1 Outline of Lab 3 1. Review of Lab 2 2. User defined function 3. Execution control 4. Plots and graphs using Matlab 5. Basic manipulation in image processing 6. Color image compression 2 Outline of Lab 3 1. Review of Lab 2 2. User defined function 3. Execution control 4. Plots and graphs using Matlab 5. Basic manipulation in image processing 6. Color image compression 3 Review of Lab 2 about Data Structure 1. Introduction the basic data structure in Matlab • Vector and Array 2. The distinct attributes of these data structures • Numerical Value • Index 4 Review of Lab 2 about Data Structure 3. What we have learnt • How to create them • For example: A = [3, 4, 5; 6, 5.5, 0] • How to manipulate them • For example: A(1,1:2) = A(1,1:2) + 2 • How to access their elements • For example: B = A(1,1:2) • How to perform mathematical and logical operations on them • For example: C = A./2.5; D = sum(sum(A)) • For example: E = A > 0.5 5 Review of Lab 2 about M file • “File” - “New” - “Script”/“Function” 6 Review of Lab 2 about M file • Open the M-file’s and run it. 7 Outline of Lab 3 1. Review of Lab 2 2. User defined function 3. Execution control 4. Plots and graphs using Matlab 5. Basic manipulation in image processing 6. Color image compression 8 User-Defined Function • A function is a piece of computer code that accepts an input argument from the user and provides output to the program. You may wish to define your own functions-those which are used commonly in your programming. User -defined functions are stored as M-files and can be accessed by Matlab if they are in the current directory. 9 User-Defined Function • Each function consists of a name, user-provided input, and calculated output. For example, the function: my_function(x) – is named my_function, – takes user input inside the parentheses (in this case, x), and – calculate a result. • The user does not see the calculations performed, but just accepts the answer. The function could be regarded as a black box. input output function 10 User-Defined Function • User-defined functions are stored as M-files. Each must start with a function definition line that contains Ø the word “function”, Ø one or several variables that defines the function output, Ø a function name, and Ø one or several variables used for the input argument. Ø Save the function as M-file using the same name in your function • Some examples: • function output = my_function(x) • function [output1, output2] = my_function(x1,x2,x3) • Hints: The function name and the names of the input and output variables are arbitrary and selected by the programmer, but the word “function” can not be changed. 11 User-Defined Function function output = my_poly(x) % This function calculates the value of a third-order comments % polynomial output = 3*x.^3+5*x.^2-2*x+1 Save above commands into an M-file and then type the below commands in command window to observe the results: >> a=4; >> my_poly(a) >> b=1:5; >> my_poly(b) 12 Outline of Lab 3 1. Review of Lab 2 2. User defined function 3. Execution control 4. Plots and graphs using Matlab 5. Basic manipulation in image processing 6. Color image compression 13 Objectives of Execution Control • Why need execution control – You may want to execute some parts of the code under certain circumstances only; – You may want to repeat a section of code a certain number of times. 14 Conditional Execution • General concepts • if statements 15 General Concepts • Generally speaking, the statements written in our scripts have been executed in sequence from the top (the first line) to the bottom (the last line). • However, it is frequently necessary to make choices about how to process a set of data based on some characteristic of that data. 16 if Statements • A set of statements (the code block to be executed) is shown as a rectangle, a if false decision point is shown as a diamond, condition and the flow of program control is indicated by arrows. true • The execution of a code block is based statements on some conditional test. If the result of the test is true, the code block is executed. Otherwise, the code block is omitted and the instructions after the A simple if statement end of that code block are executed. 17 if Statements Examples: Suppose Test =[2 0 0 0 ; 3 0 0 0; 4 0 0 0; 0 0 0 1] Type Type if Test(4,4)>0 if Test(1,1)>0 | Test(2,4)==0 display(‘Condition is true’) display(‘TRUE’) end else display(‘FALSE’) Matlab displays end ans = Condition is true Matlab displays ans = TRUE 18 if Statements if elseif false elseif false condition false condition … condition else true true true statements(1) statements(2) statements(n) statements(n+1) A compound if statement (more than one conditional test) • In the compound if statement, if the first logical test returns false, a second test is performed to determine whether the second code block should be executed. If that test returns false, as many further tests as necessary may be performed, each with the appropriate code block to be implemented when the result is true. Finally, if none of these tests returns true, the last code block (with the keyword else) is executed. 19 if Statements if elseif false elseif false condition false condition … condition else true true true statements(1) statements(2) statements(n) statements(n+1) A compound if statement (more than one conditional test) 1. If one of the code blocks is executed, the next instruction to execute is the one that follows the conditional code after the end statement. 2. In particular, if there is no else clause, it is possible that no code at all is executed in this conditional statement. 20 General Template of if Statements if <logical expression 1> <code block 1> elseif <logical expression 2> <code block 2> … elseif <logical expression n> <code block n> else <default code block> end 21 Important Notes of if Statements 1. The only essential ingredients are the first if statement, one code block, and the end statement. All other features may be added as the logical requires. 2. The code blocks may contain any sequence of legal Matlab statements, including other if statements (nested ifs). 3. Nested if statements with a code block are an alternative implementation of a logical and statement. clear all; a = 5; clear all; if a>3 a = 5; if a-4 == 1 if a>3 & a-4==1 b=6 b=6 end end 22 end Iteration • General concepts • for loops 23 General Concepts • Iteration allow controlled repetition of a code block. Control statements at the beginning of the code block specify the manner and extent of the repetition. • The for loop is designed to repeat its code block a fixed number of times and largely automates the process of managing the iteration. 24 for Loops • The repeated execution of the code block is performed under the control of a loop- for Done control variable. It is first set to an initial <loop> value that is tested against a terminating condition. If the terminating test succeeds, the program leaves the for loop. Otherwise, computations the computations in the code block are performed using the current value of that variable. When one pass through the code block is finished, the variable is updated to its next value, and control returns to Structure of a for loop the termination test. 25 General Template of for Loops for <variable specification> <code block> end • All of the mechanics of iteration control are handled automatically in the variable specification section. 26 Important Notes of for Loops • The core concept in the Matlab for loop implementation is in the style of the variable specification, which is accomplished as follows: <variable specification>: <variable> = <vector> • where <variable> is the name of the loop control variable and <vector> is any vector that can be created by the techniques discussed in this lab. 27 for Statements Example: Type for k = 1:10 Remember - the colon operator k indicates every integer between end the start and end OR Matlab displays start : increment : end k= 1 k= 2 and so on, through k = 10. Type for counter = 2:2:10 counter notice that counter increments end by 2 with each time thru the loop 28 Exercise 1 (Control Structure) Create a M-file and write these codes down. % use the percent sign to Mymatrix = [1:5; 14:2:22; 50:-7:22]; % indicate comments for i = 1:3 for j = 1:5 if Mymatrix(i,j)< 5 | Mymatrix(i,j) > 20 newmatrix(i,j) = Mymatrix(i,j); elseif Mymatrix(i,j) == 20 newmatrix(i,j) = 100; else newmatrix(i,j) = 0; %semicolon means end %Matlab won’t display end end 29 Outline of Lab 3 1. Review of Lab 2 2. User defined function 3. Execution control 4. Plots and graphs using Matlab 5. Basic manipulation in image processing 6. Color image compression 30 Basic Plotting Simple x-y plots: >> x=[0:2:18]; >> y=[0,0.33,4.13,6.29,6.85 ,11.19,13.19,13.96,16.33 ,18.17]; >> plot(x,y) Hint: The number of elements in vector x must be equal to the number of elements in vector y, otherwise it will make an error. 31 Basic Plotting Titles, Labels, and Grids: >> x=[0:2:18]; >> y=[0,0.33,4.13,6.29,6.85,11.1 9,13.19,13.96,16.33,18.17]; >> plot(x,y), title('Lab Experiment 1'), xlabel('Time'), ylabel('Distance'), grid on Hint: You must create a graph before you add the title and labels. If you specify the title and labels first, they are erased when the plot command executes. 32 Basic Plotting Line, Color, and Mark style: >> x=[0:2:18]; >> y=[0,0.33,4.13,6.29,6. 85,11.19,13.19,13.96,1 6.33,18.17]; >> plot(x,y,':ok',x,y*2,'-- xr',x,y/2,'-b') Hint: You plot three different x-y plots in one image, using different line type, point type and color. 33 Basic Plotting Line, Color, and Mark style: Line Type Indicator Point Type Indicator Color Indicator solid - point . blue b dotted : circle o green g dash-dot -. x-mark x red r dashed -- plus + cyan c star * magenta m square s yellow y diamond d black k 34 Exercise 2 (Basic Plotting) 1. Plot x versus y for y=sin(x). Let x vary from 0 to 2pi in increments of 0.1pi. 2. Add a title and labels to your plot. 3. Plot x versus y1 and y2 for y1=sin(x) and y2=cos(x). Let x vary from 0 to 2*pi in increments of 0.1*pi. Add a title and labels to your plot. 4. Re-create the plot from step 3, but make the sin(x) line dashed and red. Make the cos(x) line green and dotted. 5. Use the M file to write and run it. 35 Subplots • The subplot command allows you to subdivide the graphing window into a grid of m rows and n columns. • The function: subplot(m,n,p) split the figure into m*n matrix. The variable p identifies the portion of the window where the current plot will be drawn. 36 Subplots • For example, if the command subplot(2,2,1) is used, the window is divided into two rows and two columns, and the plot is drawn in the upper left-hand window. The windows are numbered from left to right, top to bottom. p=1 p=2 p=3 p=4 37 Subplots >> x=0:pi/20:2*pi; >> subplot(2,1,1) >> plot(x,sin(x)) >> subplot(2,1,2) >> plot(x,sin(2*x)) Simple x-y plots, in the first part of figure, y = sin(x). And in the second part of figure, y= sin(2*x). 38 Exercise 3 (Subplots) 1. Subdivided a figure window into one row and two columns. 2. In the left window, plot y=tan(x) Let x vary from -1.5 to 1.5 in increment of 0.1. 3. Add a title and axis labels to your graph. 4. In the right window, plot y=sinh(x) for the same x range. (Hyperbolic sine function) 5. Add a title and axis labels to this graph. 6. Use the M file to write and run it. 39 Histograms • A histogram is a special type of graph that is particularly useful for the statistical analysis of data. A histogram is a plot showing the distribution of a set of values. In Matlab, the histogram compute the number of values falling into 10 bins (categories) that are equally spaced between the minimum and maximum values. >> x=[100,96,74,87,75,22, 56,78,34,35,93,88,86, 42,55,48,9,6]; >> hist(x) 40 Histograms • The default number of bins is 10, but if we have a large data set, we may want to divide the data up into more bins. For example, to create a histogram with 25 bins, the command would be hist(x, 25) . 41 Exercise 4 (Histogram) Suppose that x = [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 14, 17, 19, 23, 29, 30, 31, 32, 35, 40, 57,66,67,68,80,90,91,100] 1. Subdivided a figure window into two rows and one column. 2. In the top window, plot the histogram of x with the default number of bins. 3. Add a title to your graph. 4. In the bottom window, plot the histogram of x with 20 bins. 5. Add a title to this graph. 6. Use the M file to write and run it. 42 Outline of Lab 3 1. Review of Lab 2 2. User defined function 3. Execution control 4. Plots and graphs using Matlab 5. Basic manipulation in image processing 6. Color image compression 43 The MATLAB Image Processing Toolbox • The Image Processing Toolbox is a collection of MATLAB functions (called M-functions or M-files) that extend the capability of the MATLAB environment for the solution of digital image processing problems. 44 The MATLAB Image Processing Toolbox • Including: Ø Spatial transformations and image registration Ø Linear filtering and transforms Ø Image enhancement and restoration Ø Image analysis and statistics 45 How to Find Suitable M-function? • Find it in Matlab Help. -by category -by alphabetical list • Find it on the textbook. • Find in the sub-folder in Matlab 46 Where is Image Processing Toolbox 47 Matlab Built-in Images • Path: \Matlab\R2011a\toolbox\ima ges\imdemos Y:\Win32\Matlab\R2011a\to olbox\images\imdemos coins • They are built-in images in Matlab which can be used directly. • It is very convenient to use these images to observe some image processing onion results. 48 Reading Image • Function: – imread() • Goal: – Load the image and save it as the array format. • Method: pout – I = imread(filename); – [I,map] = imread(filename); • Examples: I = imread('pout.tif'); I = imread('rice.png'); rice 49 Displaying Image • Function: – imshow() • Goal: – Open a window to show the image • Method: – imshow(I) • Goal: – Open a new window to show the image • Method: – figure,imshow(I) 50 Displaying Image(cont.) • Function: – colorbar • Goal: – To display an image with a colorbar that indicates the range of intensity values. • Method: – imshow(I), colorbar • Example: – I = imread('pout.tif'); imshow(I) , colorbar 51 Writing Image • Function: – imwrite() • Goal: – Function: write the image out as a file • Method: – imwrite(I,filename,format) • Example: – imwrite(I, ‘pout.jpg’, ‘JPEG’); 52 Image Information • Function: – size() • Goal: – Returns the number of rows and columns of an matrix/image • Method: – [M,N] = size(I) for matrix/image I, returns the number of rows and columns in X as separate output variables. • Example: – I= imread('saturn.png'); % I is a gray image [I_x,I_y] = size(I) % I_x= height of the image, I_y= width of the image • Method: – M = SIZE(X,DIM) returns the length of the dimension specified 53 Image Information • Function: – whos • Goal: – Display information about a variable . • Example: – whos I • Function: – imfinfo() • Goal: – display information about image file . • Example: – info = imfinfo('saturn.png') 54 Digital Image Processing • Function: – im2bw() • Goal: – Convert intensity image I to binary image g using threshold T, where T must be in range [0, 1]. • Method: – g = im2bw(I, T); • Example: – I= imread('pout.tif'); g = im2bw(I, 0.4); imshow(g) ,colorbar 55 Digital Image Processing (cont.) • Function: – rgb2gray() • Goal: – Transform RGB color model image into gray-level image. • Example: – I= imread ('saturn.png'); imshow(I); g = rgb2gray(I); figure, imshow(g), colorbar 56 Digital Image Processing (cont.) • Function: – imresize() • Method: • Goal: – imresize(A, [NUMROWS NUMCOLS], METHOD) – Change the size of an image. • Method: – imresize(A, SCALE, METHOD) • Example: • Example: – I = imread('circuit.tif'); – I = imread('circuit.tif'); J = imresize(I,[100 150], J = imresize(I,1.25); 'bilinear'); imshow(I) imshow(I) figure, imshow(J) figure, imshow(J) 57 Digital Image Processing (cont.) • Function: – imrotate(); • Goal: – Rotate image A by ANGLE degrees in a counterclockwise direction around its center point. • Method: – imrotate(I, angle); • Example: – I = imread('pout.tif'); J = imrotate(I,35); imshow(J) 58 More Example (cont.) • How to use this function ? – imfilter() • Find instructions about it by help – Help imfilter • Write this code and see what will happen? – I = imread('coins.png'); h = ones(5,5) / 25; I2 = imfilter(I,h); imshow(I), title('Original Image'); figure, imshow(I2), title('Filtered Image') 59 More Example (cont.) • Write this code and see what will happen? – I = imread('cameraman.tif'); h = fspecial('unsharp'); I2 = imfilter(I,h); imshow(I), title('Original Image') figure, imshow(I2), title('Filtered Image') 60 Outline of Lab 3 1. Review of Lab 2 2. User defined function 3. Execution control 4. Plots and graphs using Matlab 5. Basic manipulation in image processing 6. Color image compression 61 Color Image Compression • Color image in Matlab • General processing of color image • Color image compression 62 Color Image in Matlab • Since a color image requires three separate items of information for each pixel, a (true) color image of size m*n is represented in Matlab by an array of size m*n*3: a three dimensional array. >> x=imread('onion.png'); >> size(x) ans = 135 198 3 63 Color Image in Matlab • We can isolate each color component by the colon operator: – x(:,:,1) is the first, red component. – x(:,:,2) is the second, green component. • – x(:,:,3) components component. The coloris the third, bluecan all be viewed with ‘imshow’ function >> figure, imshow(x(:,:,1)) >> figure, imshow(x(:,:,2)) >> figure, imshow(x(:,:,3)) Red component Green component Blue component 64 The RGB components Processing of Color Images Image R Processing G B Processing Processing R’ G’ B’ Output RGB processing: process each RGB matrix separately 65 Framework of Image Compression Original image DCT Quantization Compressed image Inverse Recovered image IDCT quantization Reminder: Do not forget reprocessing of color images 66 An example >> a = imread('onion.png'); >> a1=a(1:8,1:8,1) Red component a1 = 63 67 65 68 66 64 63 66 61 61 64 66 67 67 66 66 61 61 64 65 66 65 65 67 65 63 64 65 65 63 66 64 62 60 60 65 66 64 64 64 66 64 63 64 67 67 68 65 62 64 64 67 66 63 64 65 65 60 61 66 66 61 63 66 67 DCT in Matlab >> da1=dct2(a1) da1 = 515.1250 -6.9009 -4.4097 -0.1354 5.6250 0.7751 -1.8265 0.4425 2.2310 -0.9758 -1.7370 -1.8775 -2.6824 -1.3261 1.0516 -1.0729 1.2950 1.6965 -0.8687 -3.0164 1.8362 -1.5076 -0.1527 -1.4253 2.7060 1.6665 -0.1457 1.5836 -2.0586 -1.2816 -1.3506 -1.4155 -2.6250 3.0794 1.0312 -0.4235 2.8750 0.4794 -1.1036 -0.5436 1.9798 3.9761 0.6547 -2.9155 -1.5988 -0.7426 -1.2419 -0.3764 1.4931 0.1617 2.0973 0.9814 0.1865 -0.4356 0.3687 0.4056 -3.0155 -1.2068 -1.9821 -2.1311 1.4400 -1.4382 0.0831 -1.3652 68 Quantization in Matlab Two different forms of quantization: 1. Uniform scalar quantization: All elements in a matrix are divided by the same number. 2. Non-uniform scalar quantization: All elements in a matrix are divided by different numbers. There is usually a quantization table for the compression task. 69 Uniform Scalar Quantization >> q=16; >> qa1=round(da1/q) qa1 = 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 70 Non-uniform Scalar Quantization >> q=[16 11 10 16 24 40 51 61;... 12 12 14 19 26 58 60 55;... 14 13 16 24 40 57 69 56;... 14 17 22 29 51 87 80 62;... 18 22 37 56 68 109 103 77;... 24 35 55 64 81 104 113 92;... 49 64 78 87 103 121 120 101;... 72 92 95 98 112 100 103 99]; >> qa1=round(da1./q) qa1 = 32 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 71 0 0 0 0 0 0 0 0 Inverse Quantization in Matlab >> qa2=qa1.*q qa2 = 512 -11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 72 Inverse DCT in Matlab >> da2=idct2(qa2) da2 = 62.0928 62.3832 62.9197 63.6206 64.3794 65.0803 65.6168 65.9072 62.0928 62.3832 62.9197 63.6206 64.3794 65.0803 65.6168 65.9072 62.0928 62.3832 62.9197 63.6206 64.3794 65.0803 65.6168 65.9072 62.0928 62.3832 62.9197 63.6206 64.3794 65.0803 65.6168 65.9072 62.0928 62.3832 62.9197 63.6206 64.3794 65.0803 65.6168 65.9072 62.0928 62.3832 62.9197 63.6206 64.3794 65.0803 65.6168 65.9072 62.0928 62.3832 62.9197 63.6206 64.3794 65.0803 65.6168 65.9072 62.0928 62.3832 62.9197 63.6206 64.3794 65.0803 65.6168 65.9072 73 Difference between Two Matrices Below is the difference between the original matrix and recovered matrix: >> diff=double(round(da2))-double(a1) diff = -1 -5 -2 -4 -2 1 3 0 1 1 -1 -2 -3 -2 0 0 1 1 -1 -1 -2 0 1 -1 -3 -1 -1 -1 -1 2 0 2 0 2 3 -1 -2 1 2 2 -4 -2 0 0 -3 -2 -2 1 0 -2 -1 -3 -2 2 2 1 -3 2 2 -2 -2 4 3 0 74 Next Lab Course Introduction of the final project Do final project by yourself 75

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