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1 A L L A H Slide 2 Command-Window Workspace & Directory Command- History The Matlab Command window - Finding your way around Slide 3 The Working Directory When you start Matlab it runs under a default directory/folder which is determined at the time of installation. Issue the command pwd (present working directory) to find out what your default working directory/folder is Slide 4 To change this to my own chosen directory e:/chris/my_matlabstuff, we use the command cd (change directory). The Matlab Search Path Typing the Matlab function path will show the current Matlab search path on yourcomputer. Try typing : Slide 5 Getting started - The Matlab workspace The first and simplest way to use Matlab is to enter commands at the Matlab prompt(usually one at a time) and allow Matlab to respond accordingly. Enter the following sequence of Matlab commands : Typing the command whos at the Matlab prompt shows what variables currently exist in the workspace: Slide 6 Saving, clearing and loading Data The easiest way to save the results of a Matlab session is to use the save command : Typing clear with no specific variables named will clear all variables from the workspace. Loading the previous session as a result all variable will be restored Slide 7 M files scripts and functions Typing directly into the command window is a useful way to get familiar with Matlab and is ideal for trying out relatively simple sequences of commands. the next step up is to write programs. Matlab programs are called m-files In the Matlab command window, left-click the file drop- down menu and select 1. New Blank M-file. 2 The Matlab text editor appears. 3 We type a sequence of Matlab commands : and there are two basic kinds scripts and functions Slide 8 Creating and running a Matlab Script file To run this file, type the name of the script file at the Matlab prompt and press return - Slide 9 Matlab Function Files The second kind of m-file is the Matlab function. Functions are also sequences of Matlab commands but differ from scripts in an important way. The key difference between scripts and functions is that when a function is executed only the declared output arguments are returned to the workspace. Any other variables created within the function vanish when Matlab returns to the workspace. Slide 10 To run this file and see what it does, type the following at the Matlab prompt - Slide 11 Building Expressions in Matlab Expressions in Matlab employ a combination of variables, operators, numbers and functions. For example, in the following expression :- Vectorizations: The nice thing about implicit vectorisation is that it helps to avoid ungainly loops and allows us to write more compact,intuitive expressions. Slide 12 Saving and printing graphics You use the Matlab print function with the specified options. print allows you to save an image or graph in a very large number of standard formats. Type For example, typing :- Slide 13 Programming in Matlab Creating arrays in Matlab The simplest way of creating small arrays in Matlab is to manually enter the elements in the command window, row by row. Try entering the commands given below: A vector is just a 1-D array : Slide 14 Programming in Matlab contd.. We can turn a row vector into a column vector, by using the transpose operator or, of course, transpose a 2-D array : We can also create string arrays which contain text Slide 15 Individual array elements are accessed using subscripts as follows:- We can change or correct the values of array elements by indexing them individually: Arrays can be 3-D (or have even higher dimensionality) : Slide 16 Matlab has a very important and powerful colon operator ( : ). This can be used to create vectors and for subscripting arrays. Heres a couple of examples of its use in creating vectors - We can also access groups of elements in an array by using the colon operator to specify the vector indices. Try the following :- We can also use the colon operator to extract entire rows or columns of an array:- Slide 17 Assignment Slide 18 We can easily concatenate (join together) arrays to make a larger array The arrays which form the elements of the concatenated array must be of conformable dimension i.e. the resulting array must be rectangular. For example, trying to form the arrays : Slide 19 Slide 20 Creating and Dealing With Larger Arrays Slide 21 Slide 22 The next example creates an array that randomly allocates values of 0 or 1 to the rows of an array by flipping a coin: A second way in which larger arrays can be constructed is through use of appropriate loop constructs. Slide 23 Avoiding loops Slide 24 Determining the size of arrays Slide 25 Here are some fun examples to try out : Slide 26 Relational and logical operators Try the examples below :- Try the examples for logical operators below :- Slide 27 Try some of these basic examples Slide 28 If statements If else statements Slide 29 Slide 30 Slide 31 For loop Slide 32 Slide 33 Indexing Vectors Slide 34 Indexing Vectors contd.. Slide 35 Slide 36 Slide 37 Slide 38 Image Processing Toolbox Slide 39 Introduction Collection of functions (MATLAB files) that supports a wide range of image processing operations Documentation www.mathworks.com Slide 40 Read an Image Read in an image Validates the graphic format (bmp, hdf, jpeg, pcx, png, tiff, xwd) Store it in an array clear, close all I = imread(pout.tif`); [X, map] = imread(pout.tif); Slide 41 Display an Image imshow(I) Slide 42 Check the Image in Memory whos Name Size Bytes Class ans 291x240 69840 uint8 array Grand total is 69840 elements using 69840 bytes uint8[0, 255] uint16[0, 65535] double[0, 1] Slide 43 Histogram Equalization Histogram: distribution of intensities figure, imhist(I) Equalize Image (contrast) I2 = histeq(I); figure, imshow(I2) figure, imhist(I2) Slide 44 Histogram Equalization (cont.) Slide 45 Slide 46 Write the Image Validates the extension Writes the image to disk imwrite(I2, pout2.png); imwrite(I2, pout2.png, BitDepth, 4); Slide 47 Morphological Opening Remove objects that cannot completely contain a structuring element Estimate background illumination clear, close all I = imread(rice.tif); imshow(I) background = imopen(I, strel(disk, 15)); imshow(background) Slide 48 Morphological Opening (cont.) Slide 49 Subtract Images Create a more uniform background I2 = imsubtract(I, background); figure, imshow(I2) Slide 50 Adjust the Image Contrast stretchlim computes [low hight] to be mapped into [bottom top] I3 = imadjust(I2, stretchlim(I2), [0 1]); figure, imshow(I3) Slide 51 Apply Thresholding to the Image Create a binary thresholded image 1. Compute a threshold to convert the intensity image to binary 2. Perform thresholding creating a logical matrix (binary image) level = graythresh(I3); bw = im2bw(I3, level); figure, imshow(bw) Slide 52 Apply Thresholding to the Image (cont.) Slide 53 Labeling Connected Components Determine the number of objects in the image Accuracy (size of objects, approximated background, connectivity parameter, touching objects) [labeled, numObjects] = bwlabel(bw, 4); numObjects {= 80} max(labeled(:)) Slide 54 Select and Display Pixels in a Region Interactive selection grain = imcrop(labeled) Colormap creation function RGB_label = label2rgb(labeled, @spring, c, shuffle); imshow(RGB_label); rect = [15 25 10 10]; roi = imcrop(labeled, rect) Slide 55 Object Properties Measure object or region properties graindata = regionprops(labeled, basic) graindata(51).Area{296} graindata(51).BoundingBox{142.5 89.5 24.0 26.0} graindata(51).Centroid{155.3953 102.1791} Create a vector which holds just one property for each object allgrains = [graindata.Area]; whos 1, darkens 1, darkens 1, darkens 1, darkens