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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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Retrieving image properties

We can retrieve and use many properties, such as the data type, the dimensions, the shape, and the size of bytes of an image with NumPy. Open the Python 3 interpreter by running the python3 command in the command prompt. Then, run the following statements one by one:

>>> import cv2
>>> img = cv2.imread('/home/pi/book/dataset/4.1.01.tiff', 0)
>>> print(type(img))

The following is the output of these statements:

<class 'numpy.ndarray'>

The preceding output confirms that the OpenCV imread() function read an image and stored it in NumPy's ndarray format. The following statement prints dimensions of the image it read:

>>> print(img.ndim)
2

The image is read in grayscale mode, which is why it is a two-dimensional image. It just has a single channel composed of intensities of grayscale. Now, let's see its shape:

>>> print(img.shape)
(256, 256)

The preceding...

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