Driver Drowsiness Detection

A computer vision project that aims to reduce car accidents by detecting if the driver is a wake or not then it takes a specific action. for our case, we used a recommendation system to play energetic music to help the driver stay awake and focused.

This repo hosts the command line interface and the deployment

Installation

1. Download these packages

- pip
- kaggle-cli
- python3

2. Clone the repo

git clone https://github.com/Ahmedsaed/Driver-Drowsiness-Detection.git

3. Update permissions

chmod +x run.py 

4. Create .env file

Rename .env copy to .env and add your Kaggle API Key to the file

5. install python packages

pip install -r requirements.txt

Usage

Use ./run.py -h for help

usage: run.py [-h] [-s] [-d] [-p] [-t] [--train_dir TRAIN_DIR] [--logging_level {debugging,info,warning}]

Driver Drowsiness Detection

options:
  -h, --help            show this help message and exit
  -s, --setup           Setup Kaggle API to download datasets
  -d, --download_datasets
                        Download Datasets from Kaggle
  -p, --preprocess_data
                        Apply preprocessing to images
  -t, --train_model     Train model on data
  --train_dir TRAIN_DIR
                        Path to training directory
  --logging_level {debugging,info,warning}
                        Set logging level

Setup

Run ./run.py -s -d to setup kaggle and download the datasets

The -s is used to setup directories and files. The -d is used to download and extract datasets for training and evaluation

Use --logging_level to choose a logging level from {debugging, info, warning}

Training

Run ./run.py -p -t --train_dir <training dir> to preprocess images and train the model

The -p is used to trigger image preprocessing which includes adding face landmarks, resizing and image augmentation

The -t is used to train the model and --train_dir is required when -p is given. if --train_dir is not provided the script will load precomputed-preprocessed images from ./Data/landmarks

Deploy

To host the deployment website, Run the following command:

streamlit run deploy.py

Then open the browser and go to localhost:8501

Model Architecture

model = Sequential([
            Conv2D(16, 3, activation='relu', input_shape=(145, 145, 3)),
            BatchNormalization(),
            MaxPooling2D(),
            Dropout(0.1),

            Conv2D(32, 5, activation='relu'),
            BatchNormalization(),
            MaxPooling2D(),
            Dropout(0.1),

            Conv2D(64, 10, activation='relu'),
            BatchNormalization(),
            MaxPooling2D(),
            Dropout(0.1),

            Conv2D(128, 12, activation='relu'),
            BatchNormalization(),

            Flatten(),

            Dense(128, activation='relu'),
            Dropout(0.25),
            Dense(64, activation='relu'),
            Dense(1, activation='sigmoid')
        ])