Top Python Imaging Library (PIL) Interview Questions and Answers for 2023

The Python Imaging Library (PIL) is a crucial tool for any Python developer working with images. As a popular library for image processing, it is often asked about in coding interviews. In this article I will provide the top PIL interview questions that hiring managers frequently ask along with detailed answers.

Whether you are a seasoned Python developer looking to brush up on your PIL knowledge or a beginner trying to learn the basics, this article will help you prepare for technical interviews and coding assessments Read on to learn key PIL concepts, understand how to effectively use it, and get fully prepared to ace your next Python interview!

Before getting into the interview questions, let’s take a moment to talk about what PIL is and why it’s useful.

PIL stands for Python Imaging Library. It is a free and open-source library that adds image processing capabilities to the Python programming language. PIL provides extensive support for different image file formats like JPEG, PNG, GIF, BMP and TIFF. It also enables you to perform various operations on images through Python code like resizing, cropping, rotating, blurring and more.

Some key advantages of using PIL are:

• Support for a wide variety of image file formats
• Fast and efficient image processing
• Easy to use API
• Availability of many pre-built image processing functions
• Active development and support community

Developers can easily load, change, and save images with PIL, without having to learn complicated computer vision algorithms or graphics programming. This makes it an essential tool for Python programmers who want to make image-based apps that do things like recognize faces, make thumbnails, and apply filters.

Okay, now that we know what PIL is, let’s look at some sample questions that are often asked about it.

Top PIL Interview Questions and Answers

Here are some of the most frequently asked interview questions on PIL that you should prepare for:

Q1. How do you install PIL or Pillow in Python?

PIL has been discontinued and replaced by Pillow – a more updated fork of PIL. To install Pillow, use pip:

pip install Pillow

Make sure you have pip installed beforehand. Some key advantages of Pillow over PIL are better documentation, active development and support for Python 3.

Q2. How do you open and display an image in PIL?

Here are the basic steps:

from PIL import Image# Open image img = Image.open('image.jpg')# Display imageimg.show() 

We import Image from PIL. The Image.open() function loads the image. PIL supports images in formats like PNG, JPG, BMP etc. We can then view the image with the show() method.

Q3. How can you get image size, format and mode in PIL?

from PIL import Imageimg = Image.open('image.jpg') # Get image sizew, h = img.size# Get image format format = img.format # Get image modemode = img.mode

We can access width, height, format (e.g. JPEG, PNG) and color mode (RGB, CMYK etc) through attributes of the Image object.

Q4. How do you resize an image using PIL?

Use the thumbnail() method to resize images. It keeps the aspect ratio intact:

from PIL import Imageimg = Image.open('image.jpg')# Resize to maximum 128x128 pixelsimg.thumbnail((128, 128))  # Save resized imageimg.save('resized_image.jpg') 

Alternatively, we can resize to exact dimensions with resize():

img = img.resize((200, 300)) 

But this may distort the image aspect ratio.

Q5. How can you crop, rotate and flip images using PIL?

PIL provides handy functions for basic image transformations:

from PIL import Imageimg = Image.open('image.jpg')# Crop box = (100, 100, 400, 400)img = img.crop(box) # Rotate 90 degrees counter-clockwise img = img.rotate(90)# Flip horizontally  img = img.transpose(Image.FLIP_LEFT_RIGHT)

We can pass a 4-tuple (left, upper, right, lower) to crop() and a degree value to rotate(). For flipping, use the TRANSPOSE constant.

Q6. How do you convert images to grayscale using PIL?

Use the convert() method with the ‘L’ mode parameter:

from PIL import Imageimg = Image.open('image.jpg').convert('L')

‘L’ stands for luminance or grayscale. For black and white, use ‘1’ instead of ‘L’.

Q7. How can you blur, sharpen or smooth images in PIL?

PIL provides a filter() method to apply various image filters:

from PIL import Image, ImageFilterimg = Image.open('image.jpg') # Blurblurred = img.filter(ImageFilter.BLUR)# Sharpen  sharp = img.filter(ImageFilter.SHARPEN)# Smoothsmooth = img.filter(ImageFilter.SMOOTH)

We can pass different ImageFilter constants like BLUR, SHARPEN, SMOOTH etc. to get desired effects.

Q8. How do you merge or concatenate multiple images into one using PIL?

Use the paste() method to paste one image onto another:

from PIL import Imageimg1 = Image.open('img1.jpg')img2 = Image.open('img2.jpg')# Create new image to fit both imagesnew_img = Image.new('RGB', (w1+w2, h1+h2)) # Paste img1 and img2 next to each othernew_img.paste(img1, (0, 0)) new_img.paste(img2, (w1, 0))new_img.save('merged.jpg')

Where w1, h1, w2, h2 are widths and heights of img1 and img2 respectively.

Q9. How can you add text or annotations to an image in PIL?

Use the ImageDraw module:

from PIL import Image, ImageDraw, ImageFontimg = Image.open('image.jpg')draw = ImageDraw.Draw(img)# Custom font style and sizefont = ImageFont.truetype('arial.ttf', 36) # Add text  draw.text((10, 10), "Sample Text", font=font)  img.save('text_added.jpg')

We create a Draw object and use the text() method to draw text on given coordinates. Font style and size can also be specified.

Q10. How do you save an image in PIL?

Simply use the save() method on the Image object:

from PIL import Imageimg = Image.open('image.jpg')# Operations on image...# Save image img.save('new_image.jpg')

We can specify the file format while saving using the file extension like JPG, PNG etc. PIL will auto-detect and save in that format.

Q11. How can you check the file size of an image loaded in PIL?

Use the size attribute of the Image object:

from PIL import Imageimg = Image.open('image.jpg')file_size = img.size[0] * img.size[1]

This gives the total number of pixels which equals file size since each pixel represents a byte generally.

Q12. What are the advantages of using PIL over OpenCV for image processing in Python?

Some key advantages of using PIL over OpenCV are:

• PIL provides a simpler API and is easier to learn
• OpenCV focuses more on real-time computer vision while PIL is meant for image processing
• PIL has better file format support and integration with Python imaging workflows
• PIL code tends to be faster and more lightweight compared to OpenCV
• PIL is more suited for basic image processing tasks like filters, transformations etc.

However, OpenCV has wider functionality like video processing, motion tracking etc. The choice depends on the specific use case.

Q13. How can you get pixel values of an image in PIL?

We can use the getpixel() method:

from PIL import Imageimg = Image.open('image.jpg')# Get RGB value of pixel at x=10, y=20r, g, b = img.getpixel((10, 20))

getpixel() returns a tuple with Red, Green and Blue values for the