covid_mask_detector

Presentation: https://www.youtube.com/watch?v=vu6G5XIaRZM

This project aims to, both, detect masks in a human face using a CNN, in a image of a public area, that may serve as sensor for the Corona Virus state in that region, and avaliating the impact of the use of image processing techniques in the data set of this problem, such as accuracy difference, cost/benefit ratio.

The assets used in this code includes 2 CNNs, one for mask detection coded and trained by me in the project: https://github.com/machadoprx/CoolConvNN

One for face detection: https://github.com/ipazc/mtcnn

Most of the data used in this project was obtained from: https://github.com/X-zhangyang/Real-World-Masked-Face-Dataset

The face detection can also be performed by the Viola Jones algorithm instead.

The differences between this methods can vary per image. After some testing, the VJ seen to be a good enough solution for face detection, apart from its instability (too many false positives), it may have greater accuracy than the Deep Learning approach used.

CNN Architecture for mask detection

How to use:

1. Clone the repository

$ git clone https://github.com/machadoprx/covid_mask_detector.git

2. In the cloned folder extract data_set.tar.xz to any folder.

3. Build a new image data set for training with the CoolConvNet. Note: Even if you do not want to use it for fit new data this program needs the data mean and standard deviation to predict samples accurately.

$ python3 data_set_enhancer.py make_data_set filter_name src_path

The currently available filters are laplace sharpening (laplace), adaptative histogram equalization (clahe) and none.

4. Evaluate image, detecting faces and drawing boxes (red for unmasked and green for masked)

$ python3 data_set_enhancer.py test_image filter_name detection_method src_path

The available detection methods are the Deep Learning approach (dnn), the Viola Jones algorithm (vj) and none.

About the CoolConvNet framework

In this application the CNN was previously trained for the mask detection task. For further information about the framework and its use check the CoolConvNet repository.

You may want to use the CoolConvNet to fit new data, or try others architectures. Thus, you can use the already compiled code in this repository, after parsing a data set as in step 3 of 'How to use':

$ taskset -c 0,1,2,3 ./cnn_c new [epochs] [validation_set_split]

To continue training, from saved weights (save each execution end):

$ taskset -c 0,1,2,3 ./cnn_c continue [epochs] [validation_set_split]

WARNING: The weights of the already trained CNN, for mask detection task, would be lost after the completion of the above commands.

The params.ini file define the CNN architecture, its structure can be found at the CoolConvNet repository.

Compiled on:

gcc (Clear Linux OS for Intel Architecture) 10.1.1 20200611 releases/gcc-10.1.0-174-g23dd425623

Input and output example:

Detected faces will be evaluated by the CNN, the boxes are drawn with OpenCV, for 80%+ probabilities, green for people with masks and red for unmasked.

Viola Jones detection and histogram equalization

$ python3 data_set_enhancer.py test_image clahe vj das.jpg

Deep learning detection and no filter applied

$ python3 data_set_enhancer.py test_image none dnn das.jpg