In this face-tracking tutorial you will learn to:

• install and run Python with the Anaconda package manager
• create a new Anaconda environment and install packages in it
• use the OpenCV package to do face detection on still images
• use the OpenCV package to do face-tracking with images from a webcam
• build your own SnapChat filters!

Installation instructions for Mac, Windows and Linux: https://conda.io/docs/user-guide/install/index.html

Anaconda is a package manager for Python. Packages (also called modules) are like recipe books - they have instructions for performing a specific task. Imagine you want to bake a pie: you might want to a import package with a recipe for making a crust so that you don't have to write your own instructions for doing that task. The package with the crust recipe would itself refer to other packages with recipes for things like how to churn butter or grow wheat and mill flour. In this way, it's possibile to do some cool stuff with Python without having to understand all the details about how the software is performing every task.

You can run Python and install packages without a package manager, but it's better to use one because it will make your life a lot easier. When you install a new package, Anaconda will make sure that the other packages that your new module relies on are up-to-date. Another advantage is that Anaconda allows you to create a new "environment" for each project you do: that way if an old project uses an old version of a package, you don't have to uninstall and reinstall packages each time you switch between old and new projects.

Create a new Anaconda environment called 'facetrack' and activate that environment

conda create --name facetrack
source activate facetrack

Now you'll want to install some packages that you need:

conda install opencv ipython matplotlib

If you're not using a development environment like Pycharm or VSCode, you'll also want to install a text editor (like Sublime or TextWrangler) where you can write your code.

OpenCV has a face detection tool that is based on an algorithm from 2001 called Haar Cascades. (you can read a bit about the algorithm here: https://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html)

Exercise 1: Make some simple modifications to a script for opening images on your local hard drive and detecting faces in those images.

Exercise 2: Make sure you can run the simple script for fetching video from your laptop camera and running the face detection algorithm on the captured frames.

• (intermediate-hard) apply snapchat filters to faces in images from your webcam (http://blog.stickpng.com/create-snapchat-filters-png-stickers/). You might wish to start from scratch using (https://www.twilio.com/blog/2015/11/getting-started-with-opencv-and-python-featuring-the-martian-2.html) as a reference, or you might want to make the reference solution better by incorporating rotations and skew calculated from the eye and mouth positions.
• intermediate: adapt code for adding a face to an astronaut helmet so that it uses video instead of a still image (https://www.twilio.com/blog/2015/11/getting-started-with-opencv-and-python-featuring-the-martian-2.html)
• (easy-intermediate) write a function that will convert an rgb image with a solid colour background to an image with a transparent background
• (intermediate-hard) write a program that will allow you to use opencv to draw a box on an image and to save the coordinates of the box (might be handy if you ever want to label your own face detection algoirthm training data!)
• (easy-intermediate) use pre-trained deep learning tools to label images: https://www.pyimagesearch.com/2017/08/21/deep-learning-with-opencv/
• write a program that will save a still photo 3 seconds after you wink. Your program might include a numerical countdown on the viewer window as well as some beeps so that the user knows when the photo will be taken.