By Joe Minichino, Joseph Howse
OpenCV three is a cutting-edge laptop imaginative and prescient library that permits a superb number of photograph and video processing operations. many of the extra excellent and futuristic good points reminiscent of face reputation or item monitoring are simply available with OpenCV three. studying the elemental strategies at the back of computing device imaginative and prescient algorithms, versions, and OpenCV's API will allow the advance of all types of real-world functions, together with protection and surveillance.
Starting with uncomplicated snapshot processing operations, the ebook will take you thru to complicated machine imaginative and prescient suggestions. computing device imaginative and prescient is a swiftly evolving technology whose functions within the actual international are exploding, so this ebook will attract desktop imaginative and prescient newcomers in addition to specialists of the topic eager to study the new OpenCV 3.0.0. you are going to construct a theoretical starting place of picture processing and video research, and development to the thoughts of category via laptop studying, buying the technical knowledge that may let you create and use item detectors and classifiers, or even song gadgets in videos or video digital camera feeds. eventually, the adventure will lead to the realm of man-made neural networks, besides the advance of a hand-written digits attractiveness application.
Style and approach
This booklet is a accomplished advisor to the new OpenCV three with Python to strengthen real-life computing device imaginative and prescient purposes.
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Exe/download (note that installing NumPy on Windows 64-bit is a bit tricky due to the lack of a 64-bit Fortran compiler on Windows, which NumPy depends on. com/Itseez/opencv. Run this ZIP, and when prompted, enter a destination folder, which we will refer to as
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However, as we will see in subsequent chapters, we usually want to perform operations on large slices of an image rather than on single pixels. reshape(height, width, 3) As a more complete example, let's convert bytearray, which contains random bytes to a grayscale image and a BGR image: import cv2 import numpy import os # Make an array of 120,000 random bytes. array(randomByteArray) # Convert the array to make a 400x300 grayscale image. png', grayImage) # Convert the array to make a 400x100 color image.