Today’s industries are faced with a growing demand for vision systems due to their non-invasive characteristics in inspecting product quality. These systems identify surface defects and faults, and verify components’ orientation and their measurements, etc. This book explores the vision techniques needed to design and develop your own industrial vision system with the help of Raspberry Pi.

You’ll start by reviewing basic concepts and applications of machine vision systems, followed by the preliminaries of Python, OpenCV, required libraries, and installing OpenCV for Python on Raspberry Pi. These are used when implementing image processing for the system applications. You’ll then look at interfacing techniques and some of the challenges industrial vision systems encounter, such as lighting and camera angles.

Algorithms and image processing techniques are also discussed, along with machine learning and deep learning techniques. Later chapters explain the use of GUI apps and real-time applications of Industrial vision systems. Each chapter concludes with examples and demo implementations to facilitate your knowledge of the concepts.

By the end of the book, you’ll be able to build and deploy computer vision applications with Python, OpenCV, and Raspberry Pi.

What You'll Learn

• Build and deploy industrial vision system using Raspberry Pi and Python programming
• Explore computer vision techniques using Raspberry Pi and OpenCV
• Implement popular vision techniques for industrial applications in real time
• Review modern image processing techniques such as image segmentation, thresholding, and contours

Who This Book Is For

Raspberry Pi and Python enthusiasts interested in computer vision applications; educators, industrialists, and industrial solution providers who want to design vision-based testing products with the help of Raspberry Pi

Table of Contents

Chapter 1: Introduction to Industrial Vision Systems

Chapter 2: Getting Started with Raspberry Pi

Chapter 3: Python Libraries for Image Processing

Chapter 4: Challenges in Machine Vision System

Chapter 5: Image Processing Using OpenCV

Chapter 6: Graphical User Interface with OpenCV and tkinter

Chapter 7: Feature Detection and Matching

Chapter 8: Image Segmentation

Chapter 9: Optical Character Recognition

Chapter 10: Machine Learning Techniques for Vision Application

Chapter 11: Industrial Vision System Applications

About the Authors

Dr. K. Mohaideen Abdul Kadhar completed his Undergraduate degree in Electronics and Communication Engineering and his M.Tech with a specialization in Control and Instrumentation in 2005. In 2015, he obtained a Ph.D. in Control System Design using evolutionary algorithms. He has more than 16 years of experience in teaching and research. His area of interests includes Robust control systems, Optimization techniques, computer vision and image processing, data science, python programming, and working with Raspberry Pi boards. He is currently developing customized industrial vision systems for various industrial requirements. He has been a consultant for several industries in developing machine vision systems for industrial applications, a master trainer, and delivered workshops in the control systems, computer vision, image processing, optimization techniques, data science and Python programming.

G. Anand completed his Undergraduate degree in Electronics and Communication Engineering in 2008 and his Postgraduate Degree (M.E) in Communication Systems in 2011. He has more than 10 years of experience in teaching and research. His areas of interest include Signal Processing, Image processing, Vision system, Python programming, Data science, and Machine Learning. He has also delivered workshops in signal processing, image processing and python programming.