Python In Practice - 15 Projects to Master Python - Working with Images: Computer Vision

The video tutorial introduces OpenCV, a library for computer vision tasks, and guides viewers through its installation using Anaconda or PIP. It then demonstrates setting up a Jupyter notebook for image processing, importing OpenCV, and reading images. The tutorial explains how to display images using OpenCV's imshow function and discusses potential errors and solutions. It also covers displaying images with Matplotlib, highlighting differences in color spaces between OpenCV and Matplotlib, and concludes with a brief introduction to color spaces.

What is the recommended method for installing OpenCV according to the tutorial?

• Using conda install

• Using brew install

• Using apt-get install

• Using npm install

What is the first step after installing OpenCV to start working with it in a Jupyter notebook?

• Creating a new Python script

• Importing the OpenCV library

• Downloading sample images

• Configuring the Jupyter environment

Which function is used to read an image file into a variable in OpenCV?

• cv2.imread

• cv2.readimage

• cv2.openimage

• cv2.loadimage

What is the purpose of the waitKey function in OpenCV?

• To convert the image to grayscale

• To pause the execution of the script

• To close the image display window

• To save the displayed image

What happens if you do not use the waitKey function after imshow in OpenCV?

• The image will not display

• The image will display but not respond

• The image will be saved automatically

• The image will be converted to grayscale

What is the main difference in color space between OpenCV and Matplotlib?

• OpenCV uses RGB, Matplotlib uses BGR

• OpenCV uses BGR, Matplotlib uses RGB

• Both use the same color space

• OpenCV uses CMYK, Matplotlib uses RGB

What are the primary colors used by most systems to visualize images?

• Cyan, Magenta, Yellow

• Black, White, Gray

• Red, Yellow, Blue

• Red, Green, Blue