Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
Book Image

Modern Computer Vision with PyTorch

By: V Kishore Ayyadevara, Yeshwanth Reddy

Overview of this book

Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.
Table of Contents (25 chapters)
Section 1 - Fundamentals of Deep Learning for Computer Vision
Section 2 - Object Classification and Detection
Section 3 - Image Manipulation
Section 4 - Combining Computer Vision with Other Techniques

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...