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As usual, create two directories for today's class. Create a
directory called lecture35 under activities, and
a directory called lab35 under labs.
Moving on from 2-dimensional lists, let's start talking about images. You have read in image files in the past (see Lecture 33). However, you didn't really mess with the true nature of that list. Let's see how we can work with that a little more.
Image files can be thought of as just 2-dimensional lists of pixel color values. We can even extend that thought to 3-dimensional lists, since every pixel value can be represented by a red, green, and blue values (typically abbreviated RGB). The one complexity with this representation is how to get an image file into a list of this structure. Luckily, we are giving you a module that will do just that.
The ppm.py module holds 4 functions, but
you are only concerned with two of them.
the read_image(file_name) function takes a string file
name of a PPM image, and returns a 3-dimensional list of pixel color
values. For any given pixel color list, the red value is store in
the 0th index, the green value is stored in the
1st index, and the blue value is stored in the
2nd index.
The write_image(ppm_file_name, image) takes a string
file name of where you want to write a PPM image, and a
3-dimensional list of the above form. This function will actually
write the image out to the file system, using the file name
specified in the parameters.
It is pretty expensive to film movies and TV shows on location now-a-days. However, with a little bit of Movie Magic, we can pretend to be anywhere in the world. Ever want to pretend you traveled to the expanses of Moscow? Now is your chance! Ever want to go to Delaware? Yeah… Delaware.
Use Google image search to find a green screen image, and a background image that is at least the same size, if not larger. You are going to write a program that will take these two images, and composite them together to create one image. You will need to convert these pictures to PPM files, as demonstrated in class.
Write a function overlay_greenscreen(greenscreen_list,
background_list) in a file called greenscreen.py.
This function takes two parameters: an m ×
n 2-dimensional list of RGB values (really just a
3-dimensional list of integers), and a j ×
k 2-dimensional list of RGB values (but really just a
3-dimensional list of integers), where j ≥
m and k ≥ n. This function
should modify greenscreen_list by replacing pixels that are
mostly green, with the cooresponding pixel
from background_list.
As it turns out, it's really hard to get a green screen to be exactly green. Thus, it is not sufficient to check just the value of the green portion of a given pixel. You need to make sure the green value is high enough, and that the red and blue values are low enough to make sure the color is truly "green."
Here is a very simplistic example of what you are trying to do:
+ =
The overlay_greenscreen function will need to
analyize every pixel in the greenscreen_list. Use
nested for loops to iterate over all indices for the rows
and columns.
Define three global constants
called RED_THRESHOLD, GREEN_THRESHOLD,
and BLUE_THRESHOLD. These should be integer
values in the range [0, 255]. With these variables, a given
pixel is replaced with a pixel from from the background only
if the red and blue colors are below this defined threshold,
and the green value is greater than the threshold.
You will likely have to play around with the threshold values you define for the RGB values to get a better green screen effect. The thresholds are going to be very different from what you expect. Can you figure out why this is the case?
It's one thing to use a green screen image you found on the Internet. It's another thing entirely to make your own green screen images! Luckily, we have this nice (hopefully pretty clean) chalkboard that we could you to mimic a green screen.
Take a picture with your cellphone (or a friends) of yourself standing in front of the chalkboard. Email the picture to yourself, and try to composite your image with a different image you found on the web. You will likely have to make drastic changes to your threshold values, but you should be able to get something that resembles a green screened image.
Blurring is a frequently used in fashion photography to smooth skin. In this activity you will create a miniature photoshop that can blur images.
Create a function called blur_image(image_file_name)
in a file called blur.py. The function should load the
specified image, blur the image, and save the image to the same file
name. You can blur a pixel by taking the average of the pixel's
neighbors. A pixel's neighbor's are the pixels above, below, left,
and right of a pixel. You can blur the image by blurring all of the
pixels.
The following image on the right is the result of blurring the image on the left ten times.
Create a function called blur_pixel(image, row,
col). The function should take an image, a two-dimensional
array of pixels, and the row and column of a pixel to blur. The
function should set the pixel in the image list at indices row,
col to the the average of the 4 pixels surrounding the
pixel. To average a pixel, you average the red, blue, and green
channels separately.
Be Careful! If the pixel is on the edge of the image not all of the neighbors may exist. The simple solution to this problem is to not blur edge pixels.
The blur_image function should iterate over the
row and column indices of every pixel that is not on an edge of
the image and call the blur_pixel function with the row
and column.
You can produce blurred images that better mimic a camera lens blur by using a Gaussian function to compute a weighted average. Compute a Gaussian blur by summing the result of multiplying each pixel in a 5x5 grid with the following weights.
| 1/273 | 4/273 | 7/273 | 4/273 | 1/273 |
| 4/273 | 16/273 | 26/273 | 16/273 | 4/273 |
| 7/273 | 26/273 | 41/273 | 26/273 | 7/273 |
| 4/273 | 16/273 | 26/273 | 16/273 | 4/273 |
| 1/273 | 4/273 | 7/273 | 4/273 | 1/273 |
When you have finished, create a tar file of your lab35
directory. To create a tar file, execute the following commands:
cd ~/cs120/labs tar czvf lab35.tgz lab35/
To submit your activity, go to cseval.roanoke.edu. You should
see an available assignment called Lab Assignment 35. Only
one of your pair should submit your activity. Make sure both partners
are listed in the header of your files.
Do not forget to email your partner today's files!