Drawing and Bitwise Operations
Last updated on 2024-04-26 | Edit this page
Estimated time: 90 minutes
Overview
Questions
- How can we draw on scikit-image images and use bitwise operations and masks to select certain parts of an image?
Objectives
- Create a blank, black scikit-image image.
- Draw rectangles and other shapes on scikit-image images.
- Explain how a white shape on a black background can be used as a mask to select specific parts of an image.
- Use bitwise operations to apply a mask to an image.
The next series of episodes covers a basic toolkit of scikit-image operators. With these tools, we will be able to create programs to perform simple analyses of images based on changes in colour or shape.
First, import the packages needed for this episode
PYTHON
import imageio.v3 as iio
import ipympl
import matplotlib.pyplot as plt
import numpy as np
import skimage as ski
%matplotlib widgetHere, we import the same packages as earlier in the lesson.
Drawing on images
Often we wish to select only a portion of an image to analyze, and ignore the rest. Creating a rectangular sub-image with slicing, as we did in the Working with scikit-image episode is one option for simple cases. Another option is to create another special image, of the same size as the original, with white pixels indicating the region to save and black pixels everywhere else. Such an image is called a mask. In preparing a mask, we sometimes need to be able to draw a shape - a circle or a rectangle, say - on a black image. scikit-image provides tools to do that.
Consider this image of maize seedlings:
Now, suppose we want to analyze only the area of the image containing the roots themselves; we do not care to look at the kernels, or anything else about the plants. Further, we wish to exclude the frame of the container holding the seedlings as well. Hovering over the image with our mouse, could tell us that the upper-left coordinate of the sub-area we are interested in is (44, 357), while the lower-right coordinate is (720, 740). These coordinates are shown in (x, y) order.
A Python program to create a mask to select only that area of the image would start with a now-familiar section of code to open and display the original image:
PYTHON
# Load and display the original image
maize_seedlings = iio.imread(uri="data/maize-seedlings.tif")
fig, ax = plt.subplots()
ax.imshow(maize_seedlings)We load and display the initial image in the same way we have done before.
NumPy allows indexing of images/arrays with “boolean” arrays of the
same size. Indexing with a boolean array is also called mask indexing.
The “pixels” in such a mask array can only take two values:
True or False. When indexing an image with
such a mask, only pixel values at positions where the mask is
True are accessed. But first, we need to generate a mask
array of the same size as the image. Luckily, the NumPy library provides
a function to create just such an array. The next section of code shows
how:
The first argument to the ones() function is the shape
of the original image, so that our mask will be exactly the same size as
the original. Notice, that we have only used the first two indices of
our shape. We omitted the channel dimension. Indexing with such a mask
will change all channel values simultaneously. The second argument,
dtype = "bool", indicates that the elements in the array
should be booleans - i.e., values are either True or
False. Thus, even though we use np.ones() to
create the mask, its pixel values are in fact not 1 but
True. You could check this, e.g., by
print(mask[0, 0]).
Next, we draw a filled, rectangle on the mask:
PYTHON
# Draw filled rectangle on the mask image
rr, cc = ski.draw.rectangle(start=(357, 44), end=(740, 720))
mask[rr, cc] = False
# Display mask image
fig, ax = plt.subplots()
ax.imshow(mask, cmap="gray")Here is what our constructed mask looks like: 
The parameters of the rectangle() function
(357, 44) and (740, 720), are the coordinates
of the upper-left (start) and lower-right
(end) corners of a rectangle in (ry, cx) order.
The function returns the rectangle as row (rr) and column
(cc) coordinate arrays.
Check the documentation!
When using an scikit-image function for the first time - or the fifth
time - it is wise to check how the function is used, via the scikit-image
documentation or other usage examples on programming-related sites
such as Stack Overflow. Basic
information about scikit-image functions can be found interactively in
Python, via commands like help(ski) or
help(ski.draw.rectangle). Take notes in your lab notebook.
And, it is always wise to run some test code to verify that the
functions your program uses are behaving in the manner you intend.
Variable naming conventions!
You may have wondered why we called the return values of the
rectangle function rr and cc?! You may have
guessed that r is short for row and
c is short for column. However, the rectangle
function returns mutiple rows and columns; thus we used a convention of
doubling the letter r to rr (and
c to cc) to indicate that those are multiple
values. In fact it may have even been clearer to name those variables
rows and columns; however this would have been
also much longer. Whatever you decide to do, try to stick to some
already existing conventions, such that it is easier for other people to
understand your code.
Other drawing operations (15 min)
There are other functions for drawing on images, in addition to the
ski.draw.rectangle() function. We can draw circles, lines,
text, and other shapes as well. These drawing functions may be useful
later on, to help annotate images that our programs produce. Practice
some of these functions here.
Circles can be drawn with the ski.draw.disk() function,
which takes two parameters: the (ry, cx) point of the centre of the
circle, and the radius of the circle. There is an optional
shape parameter that can be supplied to this function. It
will limit the output coordinates for cases where the circle dimensions
exceed the ones of the image.
Lines can be drawn with the ski.draw.line() function,
which takes four parameters: the (ry, cx) coordinate of one end of the
line, and the (ry, cx) coordinate of the other end of the line.
Other drawing functions supported by scikit-image can be found in the scikit-image reference pages.
First let’s make an empty, black image with a size of 800x600 pixels.
Recall that a colour image has three channels for the colours red,
green, and blue (RGB, cf. Image
Basics). Hence we need to create a 3D array of shape
(600, 800, 3) where the last dimension represents the RGB
colour channels.
Now your task is to draw some other coloured shapes and lines on the image, perhaps something like this:
Drawing a circle:
PYTHON
# Draw a blue circle with centre (200, 300) in (ry, cx) coordinates, and radius 100
rr, cc = ski.draw.disk(center=(200, 300), radius=100, shape=canvas.shape[0:2])
canvas[rr, cc] = (0, 0, 255)Drawing a line:
PYTHON
# Draw a green line from (400, 200) to (500, 700) in (ry, cx) coordinates
rr, cc = ski.draw.line(r0=400, c0=200, r1=500, c1=700)
canvas[rr, cc] = (0, 255, 0)We could expand this solution, if we wanted, to draw rectangles,
circles and lines at random positions within our black canvas. To do
this, we could use the random python module, and the
function random.randrange, which can produce random numbers
within a certain range.
Let’s draw 15 randomly placed circles:
PYTHON
import random
# create the black canvas
canvas = np.zeros(shape=(600, 800, 3), dtype="uint8")
# draw a blue circle at a random location 15 times
for i in range(15):
rr, cc = ski.draw.disk(center=(
random.randrange(600),
random.randrange(800)),
radius=50,
shape=canvas.shape[0:2],
)
canvas[rr, cc] = (0, 0, 255)
# display the results
fig, ax = plt.subplots()
ax.imshow(canvas)We could expand this even further to also randomly choose whether to
plot a rectangle, a circle, or a square. Again, we do this with the
random module, now using the function
random.random that returns a random number between 0.0 and
1.0.
PYTHON
import random
# Draw 15 random shapes (rectangle, circle or line) at random positions
for i in range(15):
# generate a random number between 0.0 and 1.0 and use this to decide if we
# want a circle, a line or a sphere
x = random.random()
if x < 0.33:
# draw a blue circle at a random location
rr, cc = ski.draw.disk(center=(
random.randrange(600),
random.randrange(800)),
radius=50,
shape=canvas.shape[0:2],
)
color = (0, 0, 255)
elif x < 0.66:
# draw a green line at a random location
rr, cc = ski.draw.line(
r0=random.randrange(600),
c0=random.randrange(800),
r1=random.randrange(600),
c1=random.randrange(800),
)
color = (0, 255, 0)
else:
# draw a red rectangle at a random location
rr, cc = ski.draw.rectangle(
start=(random.randrange(600), random.randrange(800)),
extent=(50, 50),
shape=canvas.shape[0:2],
)
color = (255, 0, 0)
canvas[rr, cc] = color
# display the results
fig, ax = plt.subplots()
ax.imshow(canvas)Image modification
All that remains is the task of modifying the image using our mask in
such a way that the areas with True pixels in the mask are
not shown in the image any more.
How does a mask work? (optional, not included in timing)
Now, consider the mask image we created above. The values of the mask
that corresponds to the portion of the image we are interested in are
all False, while the values of the mask that corresponds to
the portion of the image we want to remove are all
True.
How do we change the original image using the mask?
When indexing the image using the mask, we access only those pixels
at positions where the mask is True. So, when indexing with
the mask, one can set those values to 0, and effectively remove them
from the image.
Now we can write a Python program to use a mask to retain only the portions of our maize roots image that actually contains the seedling roots. We load the original image and create the mask in the same way as before:
PYTHON
# Load the original image
maize_seedlings = iio.imread(uri="data/maize-seedlings.tif")
# Create the basic mask
mask = np.ones(shape=maize_seedlings.shape[0:2], dtype="bool")
# Draw a filled rectangle on the mask image
rr, cc = ski.draw.rectangle(start=(357, 44), end=(740, 720))
mask[rr, cc] = FalseThen, we use NumPy indexing to remove the portions of the image,
where the mask is True:
Then, we display the masked image.
The resulting masked image should look like this:
Masking an image of your own (optional, not included in timing)
Now, it is your turn to practice. Using your mobile phone, tablet, webcam, or digital camera, take an image of an object with a simple overall geometric shape (think rectangular or circular). Copy that image to your computer, write some code to make a mask, and apply it to select the part of the image containing your object. For example, here is an image of a remote control:
And, here is the end result of a program masking out everything but the remote:
Here is a Python program to produce the cropped remote control image shown above. Of course, your program should be tailored to your image.
PYTHON
# Load the image
remote = iio.imread(uri="data/remote-control.jpg")
remote = np.array(remote)
# Create the basic mask
mask = np.ones(shape=remote.shape[0:2], dtype="bool")
# Draw a filled rectangle on the mask image
rr, cc = ski.draw.rectangle(start=(93, 1107), end=(1821, 1668))
mask[rr, cc] = False
# Apply the mask
remote[mask] = 0
# Display the result
fig, ax = plt.subplots()
ax.imshow(remote)Masking a 96-well plate image (30 min)
Consider this image of a 96-well plate that has been scanned on a flatbed scanner.
PYTHON
# Load the image
wellplate = iio.imread(uri="data/wellplate-01.jpg")
wellplate = np.array(wellplate)
# Display the image
fig, ax = plt.subplots()
ax.imshow(wellplate)
Suppose that we are interested in the colours of the solutions in each of the wells. We do not care about the colour of the rest of the image, i.e., the plastic that makes up the well plate itself.
Your task is to write some code that will produce a mask that will
mask out everything except for the wells. To help with this, you should
use the text file data/centers.txt that contains the (cx,
ry) coordinates of the centre of each of the 96 wells in this image. You
may assume that each of the wells has a radius of 16 pixels.
Your program should produce output that looks like this:
Hint: You can load data/centers.txt using:
PYTHON
# load the well coordinates as a NumPy array
centers = np.loadtxt("data/centers.txt", delimiter=" ")
# read in original image
wellplate = iio.imread(uri="data/wellplate-01.jpg")
wellplate = np.array(wellplate)
# create the mask image
mask = np.ones(shape=wellplate.shape[0:2], dtype="bool")
# iterate through the well coordinates
for cx, ry in centers:
# draw a circle on the mask at the well center
rr, cc = ski.draw.disk(center=(ry, cx), radius=16, shape=wellplate.shape[:2])
mask[rr, cc] = False
# apply the mask
wellplate[mask] = 0
# display the result
fig, ax = plt.subplots()
ax.imshow(wellplate)Masking a 96-well plate image, take two (optional, not included in timing)
If you spent some time looking at the contents of the
data/centers.txt file from the previous challenge, you may
have noticed that the centres of each well in the image are very
regular. Assuming that the images are scanned in such a way
that the wells are always in the same place, and that the image is
perfectly oriented (i.e., it does not slant one way or another), we
could produce our well plate mask without having to read in the
coordinates of the centres of each well. Assume that the centre of the
upper left well in the image is at location cx = 91 and ry = 108, and
that there are 70 pixels between each centre in the cx dimension and 72
pixels between each centre in the ry dimension. Each well still has a
radius of 16 pixels. Write a Python program that produces the same
output image as in the previous challenge, but without having
to read in the centers.txt file. Hint: use nested for
loops.
Here is a Python program that is able to create the masked image
without having to read in the centers.txt file.
PYTHON
# read in original image
wellplate = iio.imread(uri="data/wellplate-01.jpg")
wellplate = np.array(wellplate)
# create the mask image
mask = np.ones(shape=wellplate.shape[0:2], dtype="bool")
# upper left well coordinates
cx0 = 91
ry0 = 108
# spaces between wells
deltaCX = 70
deltaRY = 72
cx = cx0
ry = ry0
# iterate each row and column
for row in range(12):
# reset cx to leftmost well in the row
cx = cx0
for col in range(8):
# ... and drawing a circle on the mask
rr, cc = ski.draw.disk(center=(ry, cx), radius=16, shape=wellplate.shape[0:2])
mask[rr, cc] = False
cx += deltaCX
# after one complete row, move to next row
ry += deltaRY
# apply the mask
wellplate[mask] = 0
# display the result
fig, ax = plt.subplots()
ax.imshow(wellplate)Key Points
- We can use the NumPy
zeros()function to create a blank, black image. - We can draw on scikit-image images with functions such as
ski.draw.rectangle(),ski.draw.disk(),ski.draw.line(), and more. - The drawing functions return indices to pixels that can be set directly.