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 lanes in an image of a road

Imagine a scenario where you have to detect the lanes within an image of a road. One way to solve this is by leveraging semantic segmentation techniques in deep learning. One of the traditional ways of solving this problem using OpenCV has been using edge and line detectors. In this section, we will learn about how edge detection followed by line detection can help in identifying lanes within an image of a road.

Here, we will have outlined a high-level understanding of the strategy:

  1. Find the edges of various objects present in the image.
  2. Identify the edges that follow a straight line and are also connected.
  3. Extend the identified lines from one end of the image to the other end.

Let's code up our strategy:

The following code is available as detecting_lanes_in_the_image_of_a_road.ipynb in the Chapter18 folder of this book's GitHub repository - Be sure to copy the URL from the notebook in GitHub to avoid any...