- The corona virus Outbreak has created various changes in the lifestyle of everyone around the world.
- In those changes wearing a mask has been very vital to every individual and the same has been announced by the government and WHO.
- Detection of people who are not wearing masks is a challenge due to the large number of populations.
- This project can be used in schools, hospitals, banks, airports etc as a digitalized scanning tool.
- The dataset are separated in train and test directory.
- Train directory contains images of with_mask:658 image and without_mask:657
- Test directory contains images of with_mask:97 image and without_mask:97
- numpy: Is a Python library used for working with arrays.
- keras: Is a deep learning API written in Python, running on top of the machine learning platform TensorFlow.
- sklearn: Is a library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
- cv2: uses machine learning algorithms to search for faces within a picture. Because faces are so complicated, there isn’t one simple test that will tell you if it found a face or not. Instead, there are thousands of small patterns and features that must be matched. The algorithms break the task of identifying the face into thousands of smaller, bite-sized tasks, each of which is easy to solve. These tasks are also called classifiers.
- matplotlib: Is a cross-platform, data visualization and graphical plotting library for Python and its numerical extension NumPy.
- Convolutional Neural Network is used to train the model.
- Clone or download the zip file
- go to face-mask directory
- Then run train the cnn model script by: python3 train_cnn_model.py
fig: training CNN model
Fig: Model Accuracy and loss
- After completion of training the cnn model run: python3 test_with_live_video.py
