Filter

Log into code.cs50.io, click on your terminal window, and execute cd by itself. You should find that your terminal window’s prompt resembles the below:

$

Next execute

wget https://cdn.cs50.net/2022/fall/psets/4/filter-less.zip

in order to download a ZIP called filter-less.zip into your codespace.

Then execute

unzip filter-less.zip

to create a folder called filter-less. You no longer need the ZIP file, so you can execute

rm filter-less.zip

and respond with “y” followed by Enter at the prompt to remove the ZIP file you downloaded.

Now type

cd filter-less

followed by Enter to move yourself into (i.e., open) that directory. Your prompt should now resemble the below.

filter-less/ $

Execute ls by itself, and you should see a few files: bmp.h, filter.c, helpers.h, helpers.c, and Makefile. You should also see a folder, images/, with four BMP files. If you run into any trouble, follow these same steps again and see if you can determine where you went wrong!

Let’s now take a look at some of the files provided to you as distribution code to get an understanding for what’s inside of them.

bmp.h

Open up bmp.h (as by double-clicking on it in the file browser) and have a look.

You’ll see definitions of the headers we’ve mentioned (BITMAPINFOHEADER and BITMAPFILEHEADER). In addition, that file defines BYTE, DWORD, LONG, and WORD, data types normally found in the world of Windows programming. Notice how they’re just aliases for primitives with which you are (hopefully) already familiar. It appears that BITMAPFILEHEADER and BITMAPINFOHEADER make use of these types.

Perhaps most importantly for you, this file also defines a struct called RGBTRIPLE that, quite simply, “encapsulates” three bytes: one blue, one green, and one red (the order, recall, in which we expect to find RGB triples actually on disk).

Why are these structs useful? Well, recall that a file is just a sequence of bytes (or, ultimately, bits) on disk. But those bytes are generally ordered in such a way that the first few represent something, the next few represent something else, and so on. “File formats” exist because the world has standardized what bytes mean what. Now, we could just read a file from disk into RAM as one big array of bytes. And we could just remember that the byte at array[i] represents one thing, while the byte at array[j] represents another. But why not give some of those bytes names so that we can retrieve them from memory more easily? That’s precisely what the structs in bmp.h allow us to do. Rather than think of some file as one long sequence of bytes, we can instead think of it as a sequence of structs.

filter.c

Now, let’s open up filter.c. This file has been written already for you, but there are a couple important points worth noting here.

First, notice the definition of filters on line 10. That string tells the program what the allowable command-line arguments to the program are: b, g, r, and s. Each of them specifies a different filter that we might apply to our images: blur, grayscale, reflection, and sepia.

The next several lines open up an image file, make sure it’s indeed a BMP file, and read all of the pixel information into a 2D array called image.

Scroll down to the switch statement that begins on line 101. Notice that, depending on what filter we’ve chosen, a different function is called: if the user chooses filter b, the program calls the blur function; if g, then grayscale is called; if r, then reflect is called; and if s, then sepia is called. Notice, too, that each of these functions take as arguments the height of the image, the width of the image, and the 2D array of pixels.

These are the functions you’ll (soon!) implement. As you might imagine, the goal is for each of these functions to edit the 2D array of pixels in such a way that the desired filter is applied to the image.

The remaining lines of the program take the resulting image and write them out to a new image file.

helpers.h

Next, take a look at helpers.h. This file is quite short, and just provides the function prototypes for the functions you saw earlier.

Here, take note of the fact that each function takes a 2D array called image as an argument, where image is an array of height many rows, and each row is itself another array of width many RGBTRIPLEs. So if image represents the whole picture, then image[0] represents the first row, and image[0][0] represents the pixel in the upper-left corner of the image.

helpers.c

Now, open up helpers.c. Here’s where the implementation of the functions declared in helpers.h belong. But note that, right now, the implementations are missing! This part is up to you.

Makefile

Finally, let’s look at Makefile. This file specifies what should happen when we run a terminal command like make filter. Whereas programs you may have written before were confined to just one file, filter seems to use multiple files: filter.c and helpers.c. So we’ll need to tell make how to compile this file.

Try compiling filter for yourself by going to your terminal and running

$ make filter

Then, you can run the program by running:

$ ./filter -g images/yard.bmp out.bmp

which takes the image at images/yard.bmp, and generates a new image called out.bmp after running the pixels through the grayscale function. grayscale doesn’t do anything just yet, though, so the output image should look the same as the original yard.

One common filter is the “grayscale” filter, where we take an image and want to convert it to black-and-white. How does that work?

• Recall that if the red, green, and blue values are all set to 0x00 (hexadecimal for 0), then the pixel is black. And if all values are set to 0xff (hexadecimal for 255), then the pixel is white. So long as the red, green, and blue values are all equal, the result will be varying shades of gray along the black-white spectrum, with higher values meaning lighter shades (closer to white) and lower values meaning darker shades (closer to black).
• So to convert a pixel to grayscale, you just need to make sure the red, green, and blue values are all the same value. But how do you know what value to make them? Well, it’s probably reasonable to expect that if the original red, green, and blue values were all pretty high, then the new value should also be pretty high. And if the original values were all low, then the new value should also be low.
• In fact, to ensure each pixel of the new image still has the same general brightness or darkness as the old image, you can take the average of the red, green, and blue values to determine what shade of grey to make the new pixel.

If you apply the above algorithm to each pixel in the image, the result will be an image converted to grayscale. Write some pseudocode to help you solve this problem.

void grayscale(int height, int width, RGBTRIPLE image[height][width])
{
    // Loop over all pixels

        // Take average of red, green, and blue

        // Update pixel values
}

First, how might you loop over all pixels? Recall that the image’s pixels are stored in the two-dimensional array, image. To iterate over a two-dimensional array, you’ll need two loops, one nested inside the other.

void grayscale(int height, int width, RGBTRIPLE image[height][width])
{
    // Loop over all pixels
    for (int i = 0; i < height; i++)
    {
        for (int j = 0; j < width; j++)
        {
            // Take average of red, green, and blue

            // Update pixel values
        }
    } 
}

Now, you can use image[i][j] to access any individual pixel of the image. But how to take the average of the red, green, and blue elements? Recall each element of image is an RGBTRIPLE, which is the struct defined in bmp.h to represent a pixel. The usual syntax to access members of a struct applies, wherein image[i][j].rgbtRed will give you access to the RGBTRIPLE’s red value, image[i][j].rgbtGreen will give you access to its green value, and so on.

When you compute the average, keep in mind the values of a pixel’s rgbtRed, rgbtGreen, and rgbtBlue components are all integers. So be sure to round any floating-point numbers to the nearest integer when assigning them to a pixel value! And why might you want to divide the sum of these integers by 3.0 and not 3?

Once you’ve averaged the pixel’s red, green, and blue values into a resulting grayscale color, go ahead and update the pixel’s red, green, and blue values. By now, you’re already acquainted with the syntax for assignment!

Most image editing programs support a “sepia” filter, which gives images an old-timey feel by making the whole image look a bit reddish-brown.

• An image can be converted to sepia by taking each pixel, and computing new red, green, and blue values based on the original values of the three.
• There are a number of algorithms for converting an image to sepia, but for this problem, we’ll ask you to use the following algorithm. For each pixel, the sepia color values should be calculated based on the original color values per the below.

  sepiaRed = .393 * originalRed + .769 * originalGreen + .189 * originalBlue
  sepiaGreen = .349 * originalRed + .686 * originalGreen + .168 * originalBlue
  sepiaBlue = .272 * originalRed + .534 * originalGreen + .131 * originalBlue
  

• Of course, the result of each of these formulas may not be an integer, but each value could be rounded to the nearest integer. It’s also possible that the result of the formula is a number greater than 255, the maximum value for an 8-bit color value. In that case, the red, green, and blue values should be capped at 255. As a result, we can guarantee that the resulting red, green, and blue values will be whole numbers between 0 and 255, inclusive.

Write some pseudocode to help you solve this problem and recall the use of nested for loops to visit every pixel.

void sepia(int height, int width, RGBTRIPLE image[height][width])
{
    // Loop over all pixels
    for (int i = 0; i < height; i++)
    {
        for (int j = 0; j < width; j++)
        {
            // Compute sepia values

            // Update pixel with sepia values
        }
    } 
}

To compute the sepia values, revisit the above bullets. You have a formula to compute the sepia values, but there are still a few catches. In particular, you’ll need to…

• Round the result of each computation to the nearest integer
• Ensure the resulting value is no larger than 255

How might a function that returns the lesser of two integers come in handy while implementing sepia, particularly when you need to make sure a color’s value is no higher than 255? You’re welcome, but not required, to write a helper function of your own to do just that!

Some filters might also move pixels around. Reflecting an image, for example, is a filter where the resulting image is what you would get by placing the original image in front of a mirror.

• Any pixels on the left side of the image should end up on the right, and vice versa.
• Note that all of the original pixels of the original image will still be present in the reflected image, it’s just that those pixels may have been rearranged to be in a different place in the image.

In the reflect function, then, you’ll need to swap the values of pixels on opposite sides of a row. Write some pseudocode to help you get started:

void reflect(int height, int width, RGBTRIPLE image[height][width])
{
    // Loop over all pixels
    for (int i = 0; i < height; i++)
    {
        for (int j = 0; j < width; j++)
        {
            // Swap pixels
        }
    } 
}

Recall from lecture how we implemented swapping two values with a temporary variable. No need to use a separate function for swapping unless you would like to!

And now’s a good time to think about your nested for loops. The outer for loop iterates over every row, while the inner for loop iterates over every pixel in that row. To successfully reflect a row, though, need you iterate over every pixel in it?

There are a number of ways to create the effect of blurring or softening an image. For this problem, we’ll use the “box blur,” which works by taking each pixel and, for each color value, giving it a new value by averaging the color values of neighboring pixels.

• Consider the following grid of pixels, where we’ve numbered each pixel.

  

• The new value of each pixel would be the average of the values of all of the pixels that are within 1 row and column of the original pixel (forming a 3x3 box). For example, each of the color values for pixel 6 would be obtained by averaging the original color values of pixels 1, 2, 3, 5, 6, 7, 9, 10, and 11 (note that pixel 6 itself is included in the average). Likewise, the color values for pixel 11 would be be obtained by averaging the color values of pixels 6, 7, 8, 10, 11, 12, 14, 15 and 16.
• For a pixel along the edge or corner, like pixel 15, we would still look for all pixels within 1 row and column: in this case, pixels 10, 11, 12, 14, 15, and 16.

When implementing the blur function, you might find that blurring one pixel ends up affecting the blur of another pixel. It might be best to create a copy of image by declaring a new, two-dimensional array with code like RGBTRIPLE copy[height][width];. Then copy image into copy, pixel by pixel, with nested for loops, like so:

void blur(int height, int width, RGBTRIPLE image[height][width])
{
    // Create a copy of image
    RGBTRIPLE copy[height][width];
    for (int i = 0; i < height; i++)
    {
        for (int j = 0; j < width; j++)
        {
            copy[i][j] = image[i][j];
        }
    }
}

Now, you can read pixels’ colors from copy but write (i.e., change) pixels’ colors in image!