Farida-Hamid / Facial_Keypoints_Detection

Facial Keypoint Detection

This implementation of the P1_Facial_Keypoints is built for Udacity's Computer Vision Nanodegree.

Project Overview

This project, combines computer vision techniques and deep learning architectures to build a facial keypoint detection system. Facial keypoints include points around the eyes, nose, and mouth on a face and are used in many applications. These applications include: facial tracking, facial pose recognition, facial filters, and emotion recognition. The can look at any image, detect faces, and predict the locations of facial keypoints on each face; examples of these keypoints are displayed below.

The project is broken up into 4 main parts Python notebooks, only Notebooks 2 and 3 (and the models.py file) contain my code:

Notebook 1 : Loading and Visualizing the Facial Keypoint Data

Notebook 2 : Defining and Training a Convolutional Neural Network (CNN) to Predict Facial Keypoints

Notebook 3 : Facial Keypoint Detection Using Haar Cascades and your Trained CNN

Notebook 4 : Fun Filters and Keypoint Uses

Dependencies:

1. Clone the repository, and navigate to the downloaded folder.

git clone https://github.com/udacity/P1_Facial_Keypoints.git
cd P1_Facial_Keypoints

2. Create (and activate) a new environment, named cv-nd with Python 3.6. If prompted to proceed with the install (Proceed [y]/n) type y.

   • Linux or Mac:

   conda create -n cv-nd python=3.6
   source activate cv-nd
   

   • Windows:

   conda create --name cv-nd python=3.6
   activate cv-nd
   

   At this point your command line should look something like: (cv-nd) <User>:P1_Facial_Keypoints <user>$. The (cv-nd) indicates that your environment has been activated, and you can proceed with further package installations.

3. Install PyTorch and torchvision; this should install the latest version of PyTorch.

   • Linux or Mac:

   conda install pytorch torchvision -c pytorch 
   

   • Windows:

   conda install pytorch-cpu -c pytorch
   pip install torchvision
   

4. Install a few required pip packages, which are specified in the requirements text file (including OpenCV).

pip install -r requirements.txt

Data

All of the data needed to train a neural network is in the P1_Facial_Keypoints repo, in the subdirectory data. In this folder are training and tests set of image/keypoint data, and their respective csv files. This will be further explored in Notebook 1: Loading and Visualizing Data, and you're encouraged to look trough these folders on your own, too.

Notebooks

1. Navigate back to the repo. (Also, your source environment should still be activated at this point.)

cd
cd P1_Facial_Keypoints

2. Open the directory of notebooks, using the below command. You'll see all of the project files appear in your local environment; open the first notebook and follow the instructions.

jupyter notebook

3. Once you open any of the project notebooks, make sure you are in the correct cv-nd environment by clicking Kernel > Change Kernel > cv-nd.