This repository is created with the intention for people who will attend the PyData tutorial to have a look at the code prior to the tutorial. This OpenCV tutorial is based on version 3.0.0

This notebook was created for the tutorial during the PyData Meeting:

• Author: Rodrigo Agundez from Qualogy Solutions
• Place: Amsterdam, Papaverweg 265
• Date: Saturday, March 12, 2016
• Time: 16:15
• Room: 2

The goal of this tutorial is to build a simple face recognition system with the use of the opencv library. This tutorial is separated in three parts:

• Basic manipulation techniques of images and video using OpenCV.
• Building our data set of images.
• Training the classification model provided by OpenCV.
• Recognize never seen images by the model.
• Recognize faces from a live video feed.
• Try to trick the face recognition to classify other types of objects.

OpenCV is an open source computer vision and machine learning software library. The library includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to:

It has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.

• OpenCV (cv2)
• Numpy
• matplotlib