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OpenCV 3.x with Python By Example

You're reading from   OpenCV 3.x with Python By Example Make the most of OpenCV and Python to build applications for object recognition and augmented reality

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788396905
Length 268 pages
Edition 2nd Edition
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Authors (2):
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Gabriel Garrido Calvo Gabriel Garrido Calvo
Author Profile Icon Gabriel Garrido Calvo
Gabriel Garrido Calvo
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Contributors
Packt Upsell
Preface
1. Applying Geometric Transformations to Images FREE CHAPTER 2. Detecting Edges and Applying Image Filters 3. Cartoonizing an Image 4. Detecting and Tracking Different Body Parts 5. Extracting Features from an Image 6. Seam Carving 7. Detecting Shapes and Segmenting an Image 8. Object Tracking 9. Object Recognition 10. Augmented Reality 11. Machine Learning by an Artificial Neural Network 1. Other Books You May Enjoy

Detecting eyes


Now that we understand how to detect faces, we can generalize the concept to detect other body parts too. It's important to understand that the Viola-Jones framework can be applied to any object. The accuracy and robustness will depend on the uniqueness of the object. For example, a human face has very unique characteristics, so it's easy to train our system to be robust. On the other hand, objects like towels, clothes or books are too generic, and there are no distinguishing characteristics as such, so it's more difficult to build a robust detector.

Let's see how to build an eye detector:

import cv2 
import numpy as np 
 
face_cascade = cv2.CascadeClassifier('./cascade_files/haarcascade_frontalface_alt.xml') 
eye_cascade = cv2.CascadeClassifier('./cascade_files/haarcascade_eye.xml') 
if face_cascade.empty(): 
  raise IOError('Unable to load the face cascade classifier xml file') 
 
if eye_cascade.empty(): 
  raise IOError('Unable to load the eye cascade classifier xml file...
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