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Raspberry Pi Computer Vision Programming

Raspberry Pi Computer Vision Programming

By : Ashwin Pajankar
3.8 (4)
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Raspberry Pi Computer Vision Programming

Raspberry Pi Computer Vision Programming

3.8 (4)
By: Ashwin Pajankar

Overview of this book

Raspberry Pi is one of the popular single-board computers of our generation. All the major image processing and computer vision algorithms and operations can be implemented easily with OpenCV on Raspberry Pi. This updated second edition is packed with cutting-edge examples and new topics, and covers the latest versions of key technologies such as Python 3, Raspberry Pi, and OpenCV. This book will equip you with the skills required to successfully design and implement your own OpenCV, Raspberry Pi, and Python-based computer vision projects. At the start, you'll learn the basics of Python 3, and the fundamentals of single-board computers and NumPy. Next, you'll discover how to install OpenCV 4 for Python 3 on Raspberry Pi, before covering major techniques and algorithms in image processing, manipulation, and computer vision. By working through the steps in each chapter, you'll understand essential OpenCV features. Later sections will take you through creating graphical user interface (GUI) apps with GPIO and OpenCV. You'll also learn to use the new computer vision library, Mahotas, to perform various image processing operations. Finally, you'll explore the Jupyter Notebook and how to set up a Windows computer and Ubuntu for computer vision. By the end of this book, you'll be able to confidently build and deploy computer vision apps.
Table of Contents (15 chapters)
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Chapter 9: Image Restoration, Segmentation, and Depth Maps

In the previous chapter, we demonstrated how to use high-pass filters and their applications in algorithms to detect edges.

In this chapter, we will learn about a few more advanced processing techniques regarding images. First, we will get started with the restoration of damaged or degraded images. Then, we will explore the fundamentals of various types of segmentation techniques. We have already seen that thresholding is a basic form of segmentation. We will explore this concept in more detail in this chapter. Finally, we will compute the disparity map and estimate the depths of objects in an image.

In this chapter, we will cover the following topics:

  • Restoring damaged images using inpainting
  • Segmenting images
  • Disparity maps and depth estimation

By the end of this chapter, we will be able to restore damaged images, apply various segmentation algorithms to images, and estimate the depth of objects...

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