Logistics

• Due date: Monday September 30, 9:00am Eastern Time.
• Late Policy: There is a one hour grace period to deal with any technical difficulties and last-minute hiccups. If you require an extension beyond that for any reason (mental ailments are just as important as physical ailments!), there is a form you must fill out prior to the deadline. This applies to students with accessibility accommodations as well. Please see the syllabus for more information.
• This assignment must be done individually. The work you submit must be your own. Please review the Academic Integrity in CSC110 page on Quercus and the honour_code.txt file included in the starter files on MarkUs (more below) which gives an overview of common mistakes students make in regards to academic integrity.
• You will submit your assignment solutions on MarkUs (see Submission instructions at the end of this handout).

Advice for your first assignment

Assignments are meant to evaluate not just the basic knowledge and skills you’ve learned, but also your ability to apply your learning to new domains, and to synthesize multiple concepts and skills to solve more complex problems.

Warning: we don’t recommend trying to complete assignments in one sitting. You’ll want to make sure you take breaks so that you don’t burn yourself out, and often you might get stuck on something and want to return to it in a few days, perhaps after having asked questions on our discussion board or during office hours. Instead you should schedule time to work on the assignment (put it in your calendar!). You’ll typically have around two weeks to work on each assignment. Even if you aren’t ready to start an assignment the day it comes out, you can plan out your next two weeks to find time to work on the assignment.

Assignments are the biggest type of assessments you’ll complete in CSC110, and their scale can feel a bit intimidating at first.

To help you, we typically break assignments down into a few different parts, where each part consists of a number of questions/problems to solve.

As you schedule time to work on the assignment, you might find it useful to set a deadline for each part separately.

For example, for Assignment 1 you might say:

• I’ll finish Part 1 by Wednesday
• I’ll finish Part 2 by next Monday
• I’ll finish Problem Set 1 by next Thursday
• I’ll spend the last few days reviewing and improving my work before making my final submission.

Getting Started

To obtain the starter files for this assignment:

1. Click this link to download the starter files for this assignment. This will download a zip file to your computer.
2. Extract the contents of this zip file into your csc110/assignments/ folder.
3. You should see a new a1 folder that contains the assignment’s starter files!

You can then see these files in your PyCharm csc110 project.

Instructions for Programming Questions and PythonTA

Your programming question work should be completed in the provided starter files.

We have provided code at the bottom of some of these files for running doctest examples (as discussed in lecture Week 2C) and running a program called PythonTA. This program checks your work for common logical, design, and style errors, and the feedback from PythonTA will be used to determine a portion of your grade for all programming parts of this assignment.

Part 1: Colour Rows 🌈

In Section 1.8 of the Course Notes, we discuss how to represent colours as three integers \((r, g, b)\), representing values for the amount of red, green, and blue used to form the colour.

Some examples are shown in the table below.

In this part of the assignment, you’ll apply your knowledge of Python comprehensions to process collections of RGB colour representations in interesting ways, and eventually build up a small Python program that allows you to manipulate real images!

First, we’ll define a colour row (or pixel row) to be a list of RGB triples, represented in Python as a list of lists, where each inner list has exactly three elements.

Here is an example of a colour row as a Python expression:

[[0, 255, 200], [1, 2, 3], [100, 100, 100], [181, 57, 173], [33, 0, 197]]

As the name “pixels” suggests, we’ll see later how an image is composed of multiple colour rows, but in this part we’ll keep things simple and write code that operates on just a single colour row.

0. Warmup. Open the a1_part1.py starter file. At the top of the file we’ve provided a function warmup1 and instructions on how to complete it. Follow the instructions and then run the file in the Python Console, and call warmup1(). You should see a Pygame window appear with the colours you specified!

   
   

   Tip: as you complete the functions in this part of the assignment, you’ll want to check your work! You can use the doctest examples we provided, your own examples in the Python console, and/or call the provided a1_helpers.show_colours_pygame function to visualize your colour rows. Enjoy!

1. Cropping rows. Now, complete the functions crop_row, crop_row_border_single, and crop_row_border_multiple. Each of these functions will require you to take a colour row and return just part of that row.

   Hint: You can do this using list slicing (a Python feature we briefly mentioned during our Week 1 lectures), or using comprehensions (such as in Exercise 3 of the Week 1 Worksheet). Note: You may only use Python features covered so far in the course, as the goal of this assignment is to evaluate your understanding of those specific topics.

2. Changing colours. Next, let’s look at how we can transform colour rows to create new rows. Implement the functions remove_red_in_row, greyscale, and sepia again using comprehensions as well as any built-in functions you may think would be useful (e.g. sum, min, max, etc.).

   Hint: if you have an RGB list colour, you can access its individual red, green, and blue values by the indexing expressions colour[0], colour[1], and colour[2], respectively.

3. Distorting colours (pixelate_row). Finally, implement the function pixelate_row. This is the most challenging function on this assignment, and you’ll need to combine the techniques you used in the previous two questions to complete them.

   Hint: the range function, and comprehensions with multiple fors may be useful here (review Exercise 2 and the Additional Exercises from the Week 2A worksheet)

Part 2: Working with Image Data

In the previous part, you completed a series of functions that transform a row of colours. While we hope you had fun visualizing your results using Pygame, this may have felt a little unsatisfying: coloured squares is one thing, but what about real images? In this part of Assignment 1, you’ll apply the functions you wrote in Part 1 to work on real image data!

0. Warmup. Open the a1_part2.py starter file. At the top of the file we’ve provided a function warmup2, which will return the pixel data for a small 30-by-15 image file we’ve provided under images/spiderman.png.

   1. First, try running this file in the Python console and then calling warmup2(). You should see a very long list of colour lists! In fact, \(30 \times 15 = 450\) lists are displayed in total.

      >>> warmup2()
      [[125, 125, 125], [81, 87, 78], [103, 114, 117], [144, 151, 164], ...

   2. Let’s use the Python library pprint to make the output look a bit nicer. In the Python console, type in the following:

      >>> image_data = warmup2()
      >>> import pprint
      >>> pprint.pprint(image_data)
      [[[125, 125, 125],
        [81, 87, 78],
        [103, 114, 117],
        [144, 151, 164],
        ...

      Better! Notice that the triple [[[ at the start. This confirms that the image data is a list of colour rows, or list of list of lists; if you scroll down, you’ll see the “breaks” where one of the colour rows ends and another begins.

      We can check the total number of colour rows contained in image_data using the len function:

      >>> len(image_data)
      15

      This number corresponds to the height of the image.

   3. Because image_data is a list, we can use list indexing to access the individual colour rows it contains. Try typing the following into the Python console to access the topmost colour row in the image:

      >>> image_data[0]
      [[125, 125, 125], [81, 87, 78], [103, 114, 117], [144, 151, 164], ...

      And because a colour row is itself a list, we can also find its size, which is the number of pixels in a single row of the image—i.e., the image’s width.

      >>> len(image_data[0])
      30

   4. Finally, inside the body of warmup2, try uncommenting (i.e., deleting the # and space) the a1_helpers.show_colour_rows.pygame function call, and then re-running the file and calling warmup2 again. You should see a new Pygame window appear showing the individual colours of the image! If you have the patience and time, you can count to make sure there are indeed 15 rows displayed, each with 30 squares.

      Note: the Python interpreter will wait for you to close the Pygame window before continuing to the return statement of the function. This means you won’t be able to see/access the colour rows in the Python console until you close the Pygame window!

1. Practice transforming multiple colour rows. Now that you’ve explored the data types we use to represent image data, let’s actually get to transforming them! We provide you with five completed functions that directly rely on your functions from part 1: remove_red_in_image, greyscale_image, sepia_image, pixelate_rows_in_image, and crop_rows_in_image.

   Each of these functions use a list comprehension together with one of your functions from Part 1 to apply a transformation to each colour row in image data.

   We can use these functions to play around and see how all of your work can be used on some real images!

   Near the bottom of a1_part2.py, we’ve provided the skeleton of a function (transform_image) that you can use to read in an image file, perform some transformations, and then save the file.

   Experiment with this function on the provided files images/spiderman.png and images/sadias_zoo.jpeg, or copy your own images into the images/ folder and use them instead!

   This part is not for credit (we aren’t grading any changes you make to the transform_image function, except checking the code with PythonTA), so feel free to spend as much or as little time as you like on it.

   But if someone asks you what you’re working on for your first assignment in CSC110, you can use this function to show off images you’ve made! 😎

2. Generalized cropping. Implement the function crop_image, which enables cropping out both columns and rows.

   This last function follows a similar structure to the transformation functions we already completed for you, but you’ll need to do something a bit different to select only a subset of the rows to keep.

   We have provided one constant EXAMPLE_PIXEL_GRID that you might find helpful to use in your own tests. You can also uncomment the line in transform_image which calls this function once you are done, to test your work out with real images.

   You may use comprehensions or list slicing for this function. (For extra practice, try both ways!)

Submission instructions

Please proofread and test your work carefully before your final submission! As we explain in Running and Testing Your Code Before Submission, it is essential that your submitted code not contain syntax errors. Python files that contain syntax errors will receive a grade of 0 on all automated tests (which is what we are using to grade this assignment). You have lots of time to work on this assignment and check your work (and right-click -> “Run in Python Console”), so please make sure to do this regularly and fix syntax errors right away.

1. Login to MarkUs and go to the CSC110 course.

2. Go to Assignment 1, and the “Submissions” tab.

3. Submit the following files: honour_code.txt, a1_part1.py and a1_part2.py. Please note that MarkUs is picky with filenames, and so your filenames must match these exactly, including using lowercase letters.

4. Refresh the page, and then download each file to make sure you submitted the right version.

Remember, you can submit your files multiple times before the due date. So you can aim to submit your work early, and if you find an error or a place to improve before the due date, you can still make your changes and resubmit your work.

After you’ve submitted your work, please give yourself a well-deserved pat on the back and go take a rest or do something fun or look at some art or eat some chocolate!