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Command-Window Workspace & Directory Command- History The Matlab Command window - Finding your way around

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 –

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 :–

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:–

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

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

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 -

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.

To run this file and see what it does, type the following at the Matlab prompt -

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.

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 :-

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 :–

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 –

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) :–

Matlab has a very important and powerful colon operator ( : ). This can be used to create vectors and for subscripting arrays. Here’s 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:-

Assignment

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 :–

Creating and Dealing With Larger Arrays

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.

Avoiding loops

Determining the size of arrays

Here are some fun examples to try out :–

Relational and logical operators Try the examples below :- Try the examples for logical operators below :-

Try some of these basic examples –

If statements If else statements

For loop

Indexing Vectors

Indexing Vectors contd..

Image Processing Toolbox

Introduction Collection of functions (MATLAB files) that supports a wide range of image processing operations Documentation

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’);

Display an Image imshow(I)

Check the Image in Memory whos Name Size Bytes Class ans 291x uint8 array Grand total is elements using bytes uint8[0, 255] uint16[0, 65535] double[0, 1]

Histogram Equalization Histogram: distribution of intensities figure, imhist(I) Equalize Image (contrast) I2 = histeq(I); figure, imshow(I2) figure, imhist(I2)

Histogram Equalization (cont.)

Write the Image Validates the extension Writes the image to disk imwrite(I2, ’pout2.png’); imwrite(I2, ‘pout2.png’, ‘BitDepth’, 4);

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)

Morphological Opening (cont.)

Subtract Images Create a more uniform background I2 = imsubtract(I, background); figure, imshow(I2)

Adjust the Image Contrast stretchlim computes [low hight] to be mapped into [bottom top] I3 = imadjust(I2, stretchlim(I2), [0 1]); figure, imshow(I3)

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)

Apply Thresholding to the Image (cont.)

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(:))

Select and Display Pixels in a Region Interactive selection grain = imcrop(labeled) Colormap creation function RGB_label = ‘c’, ‘shuffle’); imshow(RGB_label); rect = [ ]; roi = imcrop(labeled, rect)

Object Properties Measure object or region properties graindata = regionprops(labeled, ‘basic’) graindata(51).Area{296} graindata(51).BoundingBox{ } graindata(51).Centroid{ } Create a vector which holds just one property for each object allgrains = [graindata.Area]; whos

Statistical Properties of Objects max(allgrains) { 695 } Return the component label of a grain size biggrain = find(allgrains == 695) { 68 } Mean grain size mean(allgrains) { 249 } Histogram (#bins) hist(allgrains, 20)

Statistical Properties of Objects (cont.)

Storage Classes double (64-bit), uint8 (8-bit), and uint16 (16-bit) Converting (rescale or offset) double im2double (automatic rescale and offsetting) RGB2 = im2uint8(RGB1); im2uint16 imapprox (reduce number of colors: indexed images)

Image Types Index Data matrix (uint8, uint16, double) Colormap matrix (m x 3 array of double [0 1]) Intensity (black = 0, white =  ) Binary (0, 1) B = logical(uint8(round(A))); (logical flag on) B = +A; (logical flag off) RGB (m x n x 3 of truecolor)

Converting Image Types dither gray2ind grayslice im2bw ind2gray ind2rgb mat2gray rgb2gray rgb2ind

Multiframe Image Arrays Same size, #planes, colormap Store separate images into one multiframe array A = cat(4, A1, A2, A3, A4, A5) Extract frames from a multiframe array FRM3 = MULTI(:, :, :, 3) Display a frame imshow(MULTI(:, :, :, 7))

Image Arithmetic imabsdiff imadd imcomplement imdivide imlincomb immultiply imsubtract

Adding Images I = imread(‘rice.tif’); J = imread(‘cameraman.tif’); K = imadd(I, J); imshow(K) Brighten an image results saturation RGB = imread(‘flowers.tif’); RGB2 = imadd(RGB, 50); subplot(1, 2, 1); imshow(RGB); subplot(1, 2, 2); imshow(RGB2);

Adding Images (cont.)

Subtracting Images Background of a scene rice = imread(‘rice.tif’); background = imopen(rice, strel(‘disk’, 15)); rice2 = imsubtract(rice, background); imshow(rice), figure, imshow(rice2); Negative values imabsdiff

Subtracting Images (cont.)

Multiplying Images Scaling: multiply by a constant (brightens >1, darkens <1) Preserves relative contrast I = imread(‘moon.tif’); J = immultiply(I, 1.2); imshow(I); figure, imshow(J)

Multiplying Images (cont.)

Dividing Images (Ratioing) I = imread(‘rice.tif’); background = imopen(I, strel(‘disk’, 15)); Ip = imdivide(I, background); imshow(Ip, []) Linear combination only truncates the final result K = imlincomb(.5, I,.5, I2);

Dividing Images (cont.)

Coordinate Systems Pixel Coordinates Discrete unit (integer) (r, c)  = (1, 1) Spatial Coordinates Continuous unit (x, y)  = (0.5, 0.5) 123

Non-default Spatial Coordinate System A = magic(5); x = [ ]; y = [ ]; image(A, ‘xData’, x, ‘yData’, y), axis image, colormap(jet(25))

Spatial Transformations Map pixel locations in an input image to new locations in an output image Resizing Rotation Cropping

Resizing Images Change the size of an image I = imread(‘ic.tif’); J = imresize(I, 1.25); K = imresize(I, [ ]); figure, imshow(J) figure, imshow(K)

Resizing Images (cont.)

Rotating Images Rotate an image by an angle in degrees I = imread(‘ic.tif’); J = imrotate(I, 35, ‘bilinear’); imshow(I) figure, imshow(J)

Rotating Images (cont.)

Cropping Images Extract a rectangular portion of an image imshow ic.tif I = imcrop;