Facemask Detection using TensorFlow and Keras
This project is a face mask classifier used to identify whether a person is wearing a facemask or not. The Neural Network was built using TensorFlow and Keras with Python.
This project is a face mask classifier used to identify whether a person is wearing a facemask or not. The Neural Network was built using TensorFlow and Keras.
Main Libraries used in this Project:-
* TensorFlow 2.3.0
* Keras 2.3.1
* OpenCV 4.2.32
Other Libraries include Numpy, sci-kit learn, and Matplotlib.
The Project has 2 Python files:
1. Model_train.py
2. mask_video.py
Dataset
The Dataset for this project was taken from https://www.kaggle.com/andrewmvd/face-mask-detection
It has 2 labels namely with mask and without a mask.
Model_train.py
This python file is used to train the Deep Learning Neural Network. The images are loaded into the files from the dataset folder. Each Image is preprocessed and converted into a Numpy array. The images are normalized and augmented using the ImageDataGenerator() function of Keras.
Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It helps reduce overfitting when training a machine learning. It is closely related to oversampling in data analysis.
The trained model is saved as mask_detector.model using Keras. The Model is built over MobileNet_v2. MobileNet-v2 is a convolutional neural network that is 53 layers deep. I added additional layers on top of MobileNet_v2 to achieve better accuracy.
Note:-
The only Prerequisite for MobileNet_v2 is that the image needs to of size 224x224. Remember to resize the image when using MobileNet_v2.
The trained model is later loaded into mask_video.py for testing.
mask_video.py
This file uses OpenCV and Numpy. The Trained Model is loaded using Keras load_model() function. Using cv2.VideoCapture() function the video from webcam is captured. The python script also uses another trained Face detector for detecting faces from Webcam Video feed.
First, a face is detected and on the detected face the facemask detector is applied to check for the presence of a mask.
Note:-
You can also use the haar cascade classifier provided my OpenCV for detecting a Face, but the accuracy provided by it is very low.
The Output folder has the output for With mask and without a mask.
How to run the Project
1. Create a python virtual environment and install the dependencies.
2. Run the Model_train.py file for training the Model.
3. Once training is completed Run mask_video.py for testing the Model.
Submitted by Shrikumar Shankar (Shri0807)
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