Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By : Joseph Howse, Joe Minichino
Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science, encompassing diverse applications 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 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and 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. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.
Table of Contents (13 chapters)

Contour detection

A vital task in computer vision is contour detection. We want to detect contours or outlines of subjects contained in an image or video frame, not only as an end in itself but also as a step toward other operations. These operations are, namely, computing bounding polygons, approximating shapes, and generally calculating regions of interest (ROIs). ROIs considerably simplify interaction with image data because a rectangular region in NumPy is easily defined with an array slice. We will be using contour detection and ROIs a lot when we explore the concepts of object detection (including face detection) and object tracking.

Let's familiarize ourselves with the API with an example:

import cv2
import numpy as np

img = np.zeros((200, 200), dtype=np.uint8)
img[50:150, 50:150] = 255

ret, thresh = cv2.threshold(img, 127, 255, 0)
contours, hierarchy = cv2.findContours...