masquito-mask-detection

overview

Machine learning part of a fullstack application to tell if you're wearing a mask right

Followed ImageAI's prediction model

Used images from Kaggle:

• Medical Masks Dataset
• Face Mask Detection

run this program

1. Clone this repository
2. Install the pip dependencies listed below
3. Download the latest model from the releases tab
4. Place the .h5 file in training_data/models/
5. Edit test_model.py's model variable to match the filename
6. Place a test image (or use an included one) in test_images/
7. Edit test_model.py's test_image variable to match the filename
8. Run the code with python ./test_model.py

dataset construction

We used two publicly available datasets from the website Kaggle. Two scripts (mmds_to_cropped.py and fmdds_to_cropped.py) cropped out the faces labelled in each dataset. check_images.py made sure there was no corrupt data. Then we combined the processed images for both datasets into aggregate/. Finally, we manually cut down each class into 270 images (limited in quantity by the improper class) and used 220:50 train:test ratio, formatted in training_data/ using ImageAI's directory structure.

scripts

mmds_to_imageai.py and fmmds_to_cropped.py

Converts the dataset from Kaggle's format to ImageAI's format

check_images.py

For an unknown reason, Pillow's Image.save function in mmds_to_imageai.py occasionally spits out unreadable data -- data that can't be parsed by Image.open. For that reason, this script finds the invalid files and deletes them. Bye-bye!

train_model.py

Trains a simple prediction model based on ImageAI's Prediction class

test_model.py

Tests the model against real-world images

directory structure

face-mask-detection-dataset/
    annotations/
    images/
medical-masks-dataset/
    medical-masks-dataset/
        labels/
        images/
cropped/
    mask/
    mask_weared_incorrect/
    none/
    poor/
    with_mask/
    without_mask/
aggregate/
    mask/
    improper/
    none/
training_data/
    json/
        model_class.json
    logs/
    models/
    test/
        mask/
        none/
        improper/
    train/
        mask/
        none/
        improper/

pip dependencies

Note: you must use Python <3.8 (I recommend 3.7).

tensorflow<2
scipy<1.5
numpy
keras
opencv-python
pillow
imageai