public/subjects/ai/emotions-detector/README.md

# Emotions detection with Deep Learning

Cameras are everywhere. Videos and images have become one of the most
interesting data sets for artificial intelligence. Image processing is a quite
broad research area, not just filtering, compression, and enhancement. Besides,
we are even interested in the question, “what is in images?”, i.e., content
analysis of visual inputs, which is part of the main task of computer vision.
The study of computer vision could make possible such tasks as 3D
reconstruction of scenes, motion capturing, and object recognition, which are
crucial for even higher-level intelligence such as image and video
understanding, and motion understanding. For this 2 months project we will
focus on two tasks:

- emotion classification
- face tracking

With the computing power exponentially increasing the computer vision field has
been developing exponentially. This is a key element because the computer power
allows using more easily a type of neural networks very powerful on images:
CNN's (Convolutional Neural Networks). Before the CNNs were democratized, the
algorithms used relied a lot on human analysis to extract features which
obviously time-consuming and not reliable. If you're interested in the "old
school methodology" [this
article](towardsdatascience.com/classifying-facial-emotions-via-machine-learning-5aac111932d3)
explains it. The history behind this field is fascinating!
[Here](https://kapernikov.com/basic-introduction-to-computer-vision/) is a
short summary of its history.

### Project goal and suggested timeline

The goal of the project is to implement a **system that detects the emotion on
a face from a webcam video stream**. To achieve this exciting task you'll have
to understand how to:

- deal with images in Python
- detect a face in an image
- train a CNN to detect the emotion on a face

That is why I suggest starting the project with a preliminary step. The goal of
this step is to understand how CNNs work and how to classify images. This
preliminary step should take approximately **two weeks**.

Then starts the emotion detection in a webcam video stream step that will last
until the end of the project !

The two steps are detailed below.

### Preliminary:

- Take [this
  course](https://www.coursera.org/learn/convolutional-neural-networks). This
  course is a reference for many reasons and one of them is the creator:
  **Andrew Ng**. He explains the basics of CNNs but also some more advanced
  topics as transfer learning, siamese networks etc ... I suggest to focus on
  Week 1 and 2 and to spend less time on Week 3 and 4. Don't worry the time
  scoping of such MOOCs are conservative. You can
  attend the lessons for free!

- Participate in [this
  challenge](https://www.kaggle.com/c/digit-recognizer/code). The MNIST dataset
  is a reference in computer vision. Researchers use it as a benchmark to
  compare their models. Start first with a logistic regression to understand
  how to handle images in Python. And then train your first CNN on this data
  set.

### Face emotions classification

Emotion detection is one of the most researched topics in the modern-day
machine learning arena. The ability to accurately detect and identify an
emotion opens up numerous doors for Advanced Human Computer Interaction. The
aim of this project is to detect up to seven distinct facial emotions in real
time. This project runs on top of a Convolutional Neural Network (CNN) that is
built with the help of Keras whose backend is TensorFlow in Python. The facial
emotions that can be detected and classified by this system are Happy, Sad,
Angry, Surprise, Fear, Disgust and Neutral.

Your goal is to implement a program that takes as input a video stream that
contains a person's face and that predicts the emotion of the person.

**Step 1**: **Fit the emotion classifier**

- Download and unzip the [data
  here](https://assets.01-edu.org/ai-branch/project3/challenges-in-representation-learning-facial-expression-recognition-challenge.zip).
  This dataset was provided for this past [Kaggle
  challenge](https://www.kaggle.com/competitions/challenges-in-representation-learning-facial-expression-recognition-challenge/overview).
  It is possible to find more information about on the challenge page.
  Train a CNN on the dataset `train.csv`. Here is an [example of
  architecture](https://www.quora.com/What-is-the-VGG-neural-network) you can
  implement.
  **The CNN has to perform more than 70% on the test set**. You can use the
  `test_with_emotions.csv` file for this. You will see that the CNNs take a lot of time
  to train. You don't want to overfit the neural network. I strongly suggest to
  use early stopping, callbacks and to monitor the training using the
  `TensorBoard`.

You have to save the trained model in `my_own_model.pkl` and to explain the
chosen architecture in `my_own_model_architecture.txt`. Use `model.summary())`
to print the architecture. It is also expected that you explain the iterations
and how you end up choosing your final architecture. Save a screenshot of the
`TensorBoard` while the model's training in `tensorboard.png` and save a plot
with the learning curves showing the model training and stopping BEFORE the
model starts overfitting in `learning_curves.png`.

- Optional: Use a pre-trained CNN to improve the accuracy. You will find some
  huge CNN's architecture that perform well. The issue is that it is expensive
  to train them from scratch. You'll need a lot of GPUs, memory and time.
  **Pre-trained CNNs** solve partially this issue because they are already
  trained on a dataset and perform well on some use cases. However, building a
  CNN from scratch is required, as mentioned, this step is optional and doesn't
  replace the first one. Similarly, save the model and explain the chosen
  architecture.

**Step 2**: **Classify emotions from a video stream**

- Use the video stream outputted by your computer's webcam and preprocess it to
  make it compatible with the CNN you trained. One of the preprocessing steps
  is: face detection. As you may have seen the training samples are imaged
  centered on a face. To do so, I suggest using a pre-trained model to detect
  faces. OpenCV for image processing tasks where we identify a face from a live
  webcam feed which is then processed and fed into the trained neural network
  for emotion detection. The preprocessing pipeline will be corrected with a
  functional test in `preprocessing_test`:

  - **Input**: Video stream of 20 sec with a face on it
  - **Output**: 20 (or 21) images cropped and centered on the face with 48 x 48
    grayscale pixels

- Predict at least one emotion per second from the video stream. The minimum
  requirement is printing in the prompt the predicted emotion with its
  associated probability. If there's any problem related to the webcam use as
  input the recorded video stream.

For that step, I suggest again to use **OpenCV** as much as possible. This link
shows how to work with a video stream with OpenCV. OpenCV documentation may
become deprecated in the future. However, OpenCV will always provide tools to
work with video streams, so search on the internet for OpenCV documentation and
more specifically "OpenCV video streams".
[Here](https://docs.opencv.org/4.x/dd/d43/tutorial_py_video_display.html) you
can find a useful tutorial to get started using OpenCV.

- Optional: **(very cool)** Hack the CNN. Take a picture for which the
  prediction of your CNN is **Happy**. Now, hack the CNN: using the same image
  **SLIGHTLY** modified make the CNN predict **Sad**.
  You can find an example on how to achieve this in [this
  article](https://medium.com/@ageitgey/machine-learning-is-fun-part-8-how-to-intentionally-trick-neural-networks-b55da32b7196)

### Deliverable

```
project
├── data
│   ├── test.csv
│   ├── train.csv
│   └── xxx.csv
├── environment.yml
├── README.md
├── results
│   ├── hack_cnn
│   │   ├── hacked_image.png
│   │   └── input_image.png
│   ├── model
│   │   ├── learning_curves.png
│   │   ├── my_own_model_architecture.txt
│   │   ├── my_own_model.pkl
│   │   ├── pre_trained_model_architecture.txt
│   │   └── pre_trained_model.pkl
│   └── preprocessing_test
│       ├── image0.png
│       ├── image1.png
│       ├── image_n.png
│       └── input_video.mp4
└── scripts
    ├── hack_the_cnn.py
    ├── predict_live_stream.py
    ├── predict.py
    ├── preprocess.py
    └── train.py

```

- Run **predict.py** expected output:

```prompt
python ./scripts/predict.py

Accuracy on test set: 72%

```

- Run **predict_live_stream.py** expected output:

```prompt
python ./scripts/predict_live_stream.py

Reading video stream ...

Preprocessing ...
11:11:11s : Happy , 73%

Preprocessing ...
11:11:12s : Happy , 93%

Preprocessing ...
11:11:13s : Surprise , 71%

Preprocessing ...
11:11:14s : Neutral , 82%

...

Preprocessing ...
11:13:29s : Happy , 63%

```

### Useful resources:

- https://machinelearningmastery.com/what-is-computer-vision/

- Use a pre-trained CNN: https://arxiv.org/pdf/1812.06387.pdf

- Hack the CNN https://medium.com/@ageitgey/machine-learning-is-fun-part-8-how-to-intentionally-trick-neural-networks-b55da32b7196

- http://ice.dlut.edu.cn/valse2018/ppt/WeihongDeng_VALSE2018.pdf

- https://arxiv.org/pdf/1812.06387.pdf