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Modern Computer Vision with PyTorch

You're reading from  Modern Computer Vision with PyTorch

Product type Book
Published in Nov 2020
Publisher Packt
ISBN-13 9781839213472
Pages 824 pages
Edition 1st Edition
Languages
Authors (2):
V Kishore Ayyadevara V Kishore Ayyadevara
Profile icon V Kishore Ayyadevara
Yeshwanth Reddy Yeshwanth Reddy
Profile icon Yeshwanth Reddy
View More author details

Table of Contents (25) Chapters

Preface Section 1 - Fundamentals of Deep Learning for Computer Vision
Artificial Neural Network Fundamentals PyTorch Fundamentals Building a Deep Neural Network with PyTorch Section 2 - Object Classification and Detection
Introducing Convolutional Neural Networks Transfer Learning for Image Classification Practical Aspects of Image Classification Basics of Object Detection Advanced Object Detection Image Segmentation Applications of Object Detection and Segmentation Section 3 - Image Manipulation
Autoencoders and Image Manipulation Image Generation Using GANs Advanced GANs to Manipulate Images Section 4 - Combining Computer Vision with Other Techniques
Training with Minimal Data Points Combining Computer Vision and NLP Techniques Combining Computer Vision and Reinforcement Learning Moving a Model to Production Using OpenCV Utilities for Image Analysis Other Books You May Enjoy Appendix

Detecting objects based on color

Green screen is a classic video editing technique where we can make someone look like they are standing in front of a completely different background. This is widely used in weather reports, where reporters point to backgrounds of moving clouds and maps. The trick in this technique is that the reporter never wears a certain color of clothing (say, green) and stands in front of a background that is only green. Then, identifying green pixels will identify what is the background and helps replace content at only those pixels.

In this section, we will learn about leveraging the cv2.inRange and cv2.bitwise_and methods to detect the green color in any given image.

The strategy that we will adopt is as follows:

  1. Convert the image from RGB into HSV space.
  2. Specify the upper and lower limits of HSV space that correspond to the color green.
  3. Identify the pixels that have a green color – this will be the mask.
  4. Perform a bitwise_and operation between the original...
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