Project Overview

This project is done as a part of capstone project for Computer vision Foundations nanodegree.

1. Create (and activate) a new environment with Python 3.5 and the `numpy` package.

	- __Linux__ or __Mac__: 
	```
	conda create --name aind-cv python=3.5 numpy
	source activate aind-cv
	```
	- __Windows__: 
	```
	conda create --name aind-cv python=3.5 numpy scipy
	activate aind-cv
	```

2. Install/Update TensorFlow (for this project, you may use CPU only).
	- Option 1: __To install TensorFlow with GPU support__, follow [the guide](https://www.tensorflow.org/install/) to install the necessary NVIDIA software on your system.  If you are using the Udacity AMI, you can skip this step and only need to install the `tensorflow-gpu` package:
	```
	pip install tensorflow-gpu -U
	```
	- Option 2: __To install TensorFlow with CPU support only__:
	```
	pip install tensorflow -U
	```

3. Install/Update Keras.

pip install keras -U

4. Switch [Keras backend](https://keras.io/backend/) to TensorFlow.
	- __Linux__ or __Mac__: 
	```
	KERAS_BACKEND=tensorflow python -c "from keras import backend"
	```
	- __Windows__: 
	```
	set KERAS_BACKEND=tensorflow
	python -c "from keras import backend"
	```

5. Install a few required pip packages (including OpenCV).

pip install -r requirements.txt

### Data

All of the data you'll need to train a neural network is in the AIND-CV-FacialKeypoints repo, in the subdirectory `data`. In this folder are a zipped training and test set of data.

1. Navigate to the data directory

cd data

2. Unzip the training and test data (in that same location). If you are in Windows, you can download this data and unzip it by double-clicking the zipped files. In Mac, you can use the terminal commands below.

unzip training.zip unzip test.zip

You should be left with two `.csv` files of the same name. You may delete the zipped files.

*Troubleshooting*: If you are having trouble unzipping this data, you can download that same training and test data on [Kaggle](https://www.kaggle.com/c/facial-keypoints-detection/data).

Now, with that data unzipped, you should have everything you need!