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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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Restoring damaged images using inpainting

The restoration of an image is the computational process of reconstructing damaged parts from existing parts of an image. If we capture an image on film with a photographic camera and develop it on paper, the photographic paper tends to degrade with the passage of time, leading to degradation of the photograph. Faulty sensors and imperfections such as dust and dirt on the camera lenses can introduce errors in the captured image. The process of transmission and reception can also introduce errors in the digital image. Image inpainting techniques can restore degraded and damaged images. Many algorithms are available to repair images. The OpenCV library implements two of the repairing methods using the cv2.inpaint() function.

This function accepts a degraded or damaged source image, a mask for image inpainting, the size of the inpainting neighborhood, and the inpainting method as arguments. The mask of inpainting is the damaged area represented...

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