This is a follow up Github repo for the facial recognition session it contains all the files mentioned in the session so feel free to look around.
To clone this repository enter the command below.
This repository contains both facial recognition using Haar cascade and also a follow up code for object detection using the model from teachable.
The code for facial recognition can be found here to run the code enter first we have the directory enter the command.
cd esparto
It is recommended to create and activate a virtual environment by following this tutorial without the virtual environment the following steps will work just fine.
To get install all the depencendices enter the following command.
pip3 install -r "requirements.txt"
Then to run the script enter the code bellow.
python face_det.py
or
python3 face_det.py
There is a model created with teachable enclosed here
The script applying the detection model on facial detection script can be executed by the following command
python det.py
or
python3 det.py
The script implementing the model created with teachable standalone can be executed by the following command
python static_label.py
or
python3 static_label.py
These are the datasets being used while making this project just for reference making your own dataset is encouraged.
├── det.py
├── face_det.py
├── h5.py
├── haarcascade_eye.xml
├── haarcascade_frontalface_default.xml
├── keras_model.h5
├── labels.txt
├── Masks
│ ├── With Mask
│ │ ├── download (1).jpg
│ │ ├── download (2).jpg
│ │ ├── download (3).jpg
│ │ ├── download (4).jpg
│ │ ├── download.jpg
│ │ ├── file-20200408-44160-1qpyrm3.jpg
│ │ ├── images (1).jpg
│ │ ├── images (2).jpg
│ │ ├── images.jpg
│ │ ├── Mask Roundup Image_0.png
│ │ └── woman-wearing-mask.original.jpg
│ └── Without Mask
│ ├── download (1).jpg
│ ├── download (2).jpg
│ ├── download (3).jpg
│ ├── download (4).jpg
│ ├── download (5).jpg
│ ├── download.jpg
│ ├── images (1).jpg
│ ├── images (2).jpg
│ ├── images (3).jpg
│ └── images.jpg
├── README.md
├── requirements.txt
├── static_label.py
├── test1.jpg
└── test2.jpg