## Description

1 Short answer problems: Do either A (if you want to use MATLAB) or B (if you want to use Python) [64 points]

A Using Matlab

1. Read through the provided Matlab introduction code and comments:

https://filebox.ece.vt.edu/~F15ECE5554ECE4984/resources/matlab.pdf.

Open an interactive session in Matlab and test the commands by typing them at the prompt. (Skip

this step if you are already familiar with Matlab.)

2. Describe (in words) the result of each of the following Matlab commands. Use the help command as

needed, but try to determine the output without entering the commands into Matlab. Do not submit

a screenshot of the result of typing these commands. [18 points]

(a) » x = randperm(1000);

(b) » a = [1,2,3; 4 5 6; 7 8 9];

» b = a(2,:);

(c) » a = [1,2,3; 4 5 6; 7 8 9];

» b = a(:);

(d) » f = randn(5,1);

» g = f(find(f > 0));

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(e) » x = zeros(1,10)+0.5;

» y = 0.5.*ones(1,length(x));

» z = x + y;

(f) » a = [1:100];

» b = a([end:-1:1]);

3. Write a few lines of code to do each of the following. Copy and paste your code into the answer sheet.

[16 points]

(a) Use rand to write a function that returns the roll of a six-sided die over N trials.

(b) Let y be the vector: y = [1 2 3 4 5 6]’. Use the reshape command to form a new matrix z

that looks like this: z =

1 3 5

2 4 6

.

(c) Use the max and find functions to set x to the maximum value that occurs in z (above), and set

r to the row number it occurs in and c to the column number it occurs in.

(d) Let v be the vector: v = [1 8 8 2 1 3 9 8]. Set a new variable x to be the number of 1’s in

the vector v.

4. Create any 100 x 100 matrix A (not all constant). Save A in a .mat file called inputAPS0Q1.mat and

submit it. Write a script which loads inputAPS0Q1.mat and performs each of the following actions on

A. Name it PS0Q1.m and submit it. [30 points]

(a) Plot all the intensities in A, sorted in decreasing value. Provide the plot in your answer sheet.

(Note, in this case we don’t care about the 2D structure of A, we only want to sort the list of all

intensities.)

(b) Display a histogram of A’s intensities with 20 bins. Again, we do not care about the 2D structure.

Provide the histogram in your answer sheet.

(c) Create a new matrix X that consists of the bottom left quadrant of A. Display X as an image

in your answer sheet using imagesc. Look at the documentation for colormap. Try colormap

gray, colormap jet, colormap copper and others. Save X in a file called outputXPS0Q1.mat

and submit the file.

(d) Create a new matrix Y, which is the same as A, but with A’s mean intensity value subtracted from

each pixel. Display Y as an image in your answer sheet using imagesc. Save Y in a file called

outputYPS0Q1.mat and submit the file.

(e) Create a new matrix Z that represents a color image the same size as A, but with 3 channels to

represent R, G and B values. Set the values to be red (i.e., R = 255, G = 0, B = 0) wherever

the intensity in A is greater than a threshold t = the average intensity in A, and black everywhere

else. Display Z as an image in your answer sheet using imagesc and imshow. Be careful with

typecasting. Save Z as outputZPS0Q1.png and submit the file. Be sure to view outputZPS0Q1.png

in an image viewer to make sure it looks right.

B Using Python

1. Read through the provided Python NumPy and Matplotlib introduction code and comments:

http://cs231n.github.io/python-numpy-tutorial/ or

https://filebox.ece.vt.edu/~F15ECE5554ECE4984/resources/numpy.pdf. Open an interactive

session in Python and test the commands by typing them at the prompt. (Skip this step if you

are already familiar with Python and NumPy.)

2. Describe (in words) the result of each of the following Python commands. Search the NumPy API documentation http://docs.scipy.org/doc/numpy/ if needed, but try to determine the output without

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entering the commands into Python. Do not submit a screenshot of the result of typing these commands. [18 points]

> import numpy as np

(a) > x = np.random.permutation(1000)

(b) > a = np.array([[1,2,3],[4,5,6],[7,8,9]])

> b = a[2,:]

(c) > a = np.array([[1,2,3],[4,5,6],[7,8,9]])

> b = a.reshape(-1)

(d) > f = np.random.randn(5,1)

> g = f[f>0]

(e) > x = np.zeros(10)+0.5

> y = 0.5*np.ones(len(x))

> z = x + y

(f) > a = np.arange(1,100)

> b = a[::-1]

3. Write a few lines of code to do each of the following. Copy and paste your code into the answer sheet.

[16 points]

(a) Use numpy.random.rand to return the roll of a six-sided die over N trials.

(b) Let y be the vector: y = np.array([1, 2, 3, 4, 5, 6]). Use the reshape command to form a

new matrix z that looks like this: [[1,2],[3,4],[5,6]]

(c) Use the numpy.max and numpy.where functions to set x to the maximum value that occurs in

z (above), and set r to the row number (0-indexed) it occurs in and c to the column number

(0-indexed) it occurs in.

(d) Let v be the vector: v = np.array([1, 8, 8, 2, 1, 3, 9, 8]). Set a new variable x to be

the number of 1’s in the vector v.

4. Create any 100 x 100 matrix A (not all constant). Save A in a .npy file called inputAPS0Q1.npy and

submit it. Write a script which loads inputAPS0Q1.npy and performs each of the following actions on

A. Name it PS0Q1.py and submit it. [30 points]

(a) Plot all the intensities in A, sorted in decreasing value. Provide the plot in your answer sheet.

(Note, in this case we don’t care about the 2D structure of A, we only want to sort the list of all

intensities.)

(b) Display a histogram of A’s intensities with 20 bins. Again, we do not care about the 2D structure.

Provide the histogram in your answer sheet.

(c) Create a new matrix X that consists of the bottom left quadrant of A. Display X as an image in

your answer sheet using matplotlib.pyplot.imshow with no interpolation (blurry effect). Look

at the documentation for matplotlib.pyplot.imshow. Save X in a file called outputXPS0Q1.npy

and submit the file.

(d) Create a new matrix Y, which is the same as A, but with A’s mean intensity value subtracted from

each pixel. Display Y as an image in your answer sheet using matplotlib.pyplot.imshow. Save

Y in a file called outputYPS0Q1.npy and submit the file.

(e) Create a new matrix Z that represents a color image the same size as A, but with 3 channels to

represent R, G and B values. Set the values to be red (i.e., R = 1, G = 0, B = 0) wherever the

intensity in A is greater than a threshold t = the average intensity in A, and black everywhere

else. Display Z as an image in your answer sheet using matplotlib.pyplot.imshow. Save Z as

outputZPS0Q1.png and submit the file. Be sure to view outputZPS0Q1.png in an image viewer

to make sure it looks right.

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2 Short programming example (you can use MATLAB or Python,

whatever you prefer) [36 points]

Choose any color image from the web or your personal collection and name it inputPS0Q2.jpg. Write a

script which performs the following transformations and displays the results in a figure using the Matlab

or Python subplot (matplotlib.pyplot.subplots) function in a 3×2 grid (3 rows and 2 columns). Each

subplot should contain the output of each of the below operations. Label each subplot with an appropriate

title. Provide the subplot in your answer sheet. Avoid using loops. Name the script PS0Q2.m(py). Note:

The transformed images should be in png format.

1. Load the input color image and swap its red and green color channels. Save the output as swapImgPS0Q2.png.

2. Convert the input color image to a grayscale image. Save the output as grayImgPS0Q2.png.

3. Perform each of the below transformations on the grayscale image produced in part 2 above.

(a) Convert the grayscale image to its negative image, in which the lightest values appear dark and

vice versa. Save the output as negativeImgPS0Q2.png.

(b) Map the grayscale image to its mirror image, i.e., flipping it left to right. Save the output as

mirrorImgPS0Q2.png.

(c) Average the grayscale image with its mirror image (use typecasting). Save the output as

avgImgPS0Q2.png.

(d) Create a matrix N whose size is same as the grayscale image, containing random numbers in

the range [0 255]. Save this matrix in a file called noise.mat(npy). Add N to the grayscale

image, then clip the resulting image to have a maximum value of 255. Save the output as

addNoiseImgPS0Q2.png.

Be sure to submit the input image, all the output images and noise.mat(npy).

Matlab Tips: Do the necessary typecasting (uint8 and double) when working with or displaying the images.

Some useful functions: title, subplot, imshow, mean, imread, imwrite, rgb2gray.

Python Tips: Do the necessary typecasting (uint8 and double) when working with or displaying the images.

If you can’t find some functions in numpy (such as rgb2gray), you can write your own function. For example:

def rgb2gray(rgb):

return np.dot(rgb[…,:3], [0.2989, 0.5870, 0.1140])1

1These are the weights that the MATLAB rbg2gray function uses.

This assignment closely follows the PS0 assignment of Kristen Grauman’s CS 376: Computer Vision at UT Austin

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