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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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Applying morphological transformations to images

Morphological operations are mathematical in nature and they change the shape of an image. These operations can best be demonstrated visually with binary images. We can apply morphological operations to eliminate a lot of unnecessary information, such as noise, in an image. A morphological operation accepts an image and a kernel as inputs. We will create a custom binary image as a binary image since this is the most suitable way to visually demonstrate morphological operations.

The mathematical morphological operation of erosion contracts the boundaries in an image. In a binary image, the white part is considered the foreground and the black part is considered the background. The erosion operation sets all the pixels on the boundary of the background part that is white to black, thus effectively shrinking the white region. Morphological dilation is the exact opposite of the erosion operation. It adds the white pixels near the boundary...

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