Deep Learning CNN Convolutional Neural Networks with Python - Padding Image

The video tutorial covers the process of adding padding to images using Python libraries such as CV2, Numpy, and Matplotlib. It begins with importing the necessary libraries, followed by reading an image and determining its dimensions. The tutorial then demonstrates how to create a padded image with black borders by adjusting the image's size and color channels. Finally, it shows how to display both the original and padded images using Matplotlib, highlighting the differences in dimensions and appearance.

Which library is primarily used for image processing in this tutorial?

• Pandas

• CV2

• TensorFlow

• Scikit-learn

What function is used to read an image in CV2?

• cv2.open()

• cv2.load()

• cv2.read()

• cv2.imread()

What is the purpose of creating a base image with padding?

• To add a border around the image

• To increase the image resolution

• To change the image format

• To convert the image to grayscale

What color are the borders added to the image?

• Blue

• Black

• Red

• White

Which function is used to convert the color format of an image in CV2?

• cv2.cvtColor()

• cv2.changeColor()

• cv2.colorChange()

• cv2.convertColor()

What is the purpose of setting up subplots in Matplotlib?

• To add padding to images

• To convert image formats

• To display multiple images in one figure

• To change image resolution

What is the shape of the original image used in the tutorial?

• 512x512

• 128x128

• 256x256

• 1024x1024

How many channels does the image have after padding?

• 2

• 1

• 3

• 4

What is the main learning objective of this tutorial?

• To study image enhancement techniques

• To learn about image compression

• To understand image padding using CV2

• To explore image segmentation

What is the final size of the padded image?

• 256x256

• 1024x1024

• 512x512

• 532x532