Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

By : Joseph Howse, Joe Minichino
5 (2)
Book Image

Learning OpenCV 5 Computer Vision with Python, Fourth Edition - Fourth Edition

5 (2)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science in the field of artificial intelligence, encompassing diverse use cases and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You'll be able to put theory into practice by building apps with OpenCV 5 and Python 3. You'll start by setting up OpenCV 5 with Python 3 on various platforms. Next, you'll learn how to perform basic operations such as reading, writing, manipulating, and displaying images, videos, and camera feeds. From taking you through image processing, video analysis, depth estimation, and segmentation, to helping you gain practice by building a GUI app, this book ensures you'll have opportunities for hands-on activities. You'll tackle two popular challenges: face detection and face recognition. You'll also learn about object classification and machine learning, which will enable you to create and use object detectors and even track moving objects in real time. Later, you'll develop your skills in augmented reality and real-world 3D navigation. Finally, you'll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age, and you'll deploy your solutions to the Cloud. By the end of this book, you'll have the skills you need to execute real-world computer vision projects.
Table of Contents (12 chapters)
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Learning OpenCV 5 Computer Vision with Python, Fourth Edition: Tackle tools, techniques, and algorithms for computer vision and machine learning
Appendix A: Bending Color Space with the Curves Filter

Modifying the application

Now that we have high-level functions and classes for several filters, it is trivial to apply any of them to the captured frames in Cameo. Let's edit and add the lines that appear highlighted in bold in the following excerpts. First, we need to add our filters module to our list of imports, as follows:

import cv2
import filters
from managers import WindowManager, CaptureManager

Now, we need to initialize any filter objects we will use. An example of this can be seen in the following modified __init__ method:

class Cameo(object):
         def __init__(self):
        self._windowManager = WindowManager('Cameo',
                                             self.onKeypress)         self._captureManager = CaptureManager(
            cv2.VideoCapture(0), self._windowManager, True)
        self._curveFilter = filters.BGRPortraCurveFilter()

Finally, we need to modify the run method in order to apply our choice of filters. Refer to the following...