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distraction_detection

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A Distraction Detector using OpenCV and Keras

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# Distraction Detector

Originally created for the Vancouver School of AI [Image Classification Workshop](https://github.com/SchoolofAI-Vancouver/learn_image_classification_2) Code Challenge.

![demo](https://raw.githubusercontent.com/johannesharmse/distraction_detection/master/additional/demo.gif)

The Distraction Detector works as follows:

1. Uses the default OpenCV [Haar Cascade Face Detector](https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml) to detect a person's face.

2. Within a detected face, the person's eyes are located using the OpenCV [Haar Cascade Eye Detector](https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_eye.xml).

3. For each detected eye, a pretrained Convolutional Neural Network(CNN) is used to predict whether a person is distracted or not(binary classifier). The [default CNN](https://github.com/johannesharmse/distraction_detection/blob/master/src/cnn/distraction_model.hdf5) was trained on eye images created by the `get_data.py` script (which essentially saves detected eyes as individual images).

Below table contains all the important scripts in this repo.

| File  | Description  |
|---|---|
| [`get_data.py`](https://github.com/johannesharmse/distraction_detection/blob/master/src/get_data.py)  | Creates training data for Distraction Classifier  |
| [`train.py`](https://github.com/johannesharmse/distraction_detection/blob/master/src/cnn/train.py)  | Creates and trains Distraction Classifier using Keras  |
| [`distraction_detector.py`](https://github.com/johannesharmse/distraction_detection/blob/master/src/distraction_detector.py)  | Detects distraction using OpenCV and Keras |

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