Real-Time Face Mask Detector with Python, OpenCV, Keras

During pandemic COVID-19, WHO has made wearing masks compulsory to protect against this deadly virus. In this tutorial we will develop a machine learning project – Real-time Face Mask Detector with Python.

We will build a real-time system to detect whether the person on the webcam is wearing a mask or not. We will train the face mask detector model using Keras and OpenCV.

Dataset

The dataset we are working on consists of 1376 images with 690 images containing images of people wearing masks and 686 images with people without masks.

 

Download the dataset: kaggle website

 

We are going to build this project in two parts. In the first part, we will write a python script using Keras to train face mask detector model. In the second part, we test the results in a real-time webcam using OpenCV.

 

Make a python file train.py to write the code for training the neural network on our dataset. Follow the steps:

 

1. Imports:

 

Import all the libraries and modules required.

 

from keras.optimizers import RMSprop

from keras.preprocessing.image import ImageDataGenerator

import cv2

from keras.models import Sequential

from keras.layers import Conv2D, Input, ZeroPadding2D, BatchNormalization, Activation, MaxPooling2D, Flatten, Dense,Dropout

from keras.models import Model, load_model

from keras.callbacks import TensorBoard, ModelCheckpoint

from sklearn.model_selection import train_test_split

from sklearn.metrics import f1_score

from sklearn.utils import shuffle

import imutils

import numpy as np

 

2. Build the neural network:

 

This convolution network consists of two pairs of Conv and MaxPool layers to extract features from the dataset. Which is then followed by a Flatten and Dropout layer to convert the data in 1D and ensure overfitting.

 

And then two Dense layers for classification.

 

model = Sequential([

    Conv2D(100, (3,3), activation='relu', input_shape=(150, 150, 3)),

    MaxPooling2D(2,2),

    

    Conv2D(100, (3,3), activation='relu'),

    MaxPooling2D(2,2),

    

    Flatten(),

    Dropout(0.5),

    Dense(50, activation='relu'),

    Dense(2, activation='softmax')

])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])

 

3. Image Data Generation/Augmentation:

 

TRAINING_DIR = "./train"

train_datagen = ImageDataGenerator(rescale=1.0/255,

                                   rotation_range=40,

                                   width_shift_range=0.2,

                                   height_shift_range=0.2,

                                   shear_range=0.2,

                                   zoom_range=0.2,

                                   horizontal_flip=True,

                                   fill_mode='nearest')

train_generator = train_datagen.flow_from_directory(TRAINING_DIR, 

                                                    batch_size=10, 

                                                    target_size=(150, 150))

VALIDATION_DIR = "./test"

validation_datagen = ImageDataGenerator(rescale=1.0/255)

validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR, 

                                                         batch_size=10, 

                                                         target_size=(150, 150))

 

4. Initialize a callback checkpoint to keep saving best model after each epoch while training:

 

checkpoint = ModelCheckpoint('model2-{epoch:03d}.model',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')

 

5. Train the model:

 

history = model.fit_generator(train_generator,

                              epochs=10,

                              validation_data=validation_generator,

                              callbacks=[checkpoint])

  

  

project code

import cv2

import numpy as np

from keras.models import load_model

model=load_model("./model-010.h5")

 

results={0:'without mask',1:'mask'}

GR_dict={0:(0,0,255),1:(0,255,0)}

 

rect_size = 4

cap = cv2.VideoCapture(0) 

 

 

haarcascade = cv2.CascadeClassifier('/home/user_name/.local/lib/python3.6/site-packages/cv2/data/haarcascade_frontalface_default.xml')

 

while True:

    (rval, im) = cap.read()

    im=cv2.flip(im,1,1) 

 

    

    rerect_size = cv2.resize(im, (im.shape[1] // rect_size, im.shape[0] // rect_size))

    faces = haarcascade.detectMultiScale(rerect_size)

    for f in faces:

        (x, y, w, h) = [v * rect_size for v in f] 

        

        face_img = im[y:y+h, x:x+w]

        rerect_sized=cv2.resize(face_img,(150,150))

        normalized=rerect_sized/255.0

        reshaped=np.reshape(normalized,(1,150,150,3))

        reshaped = np.vstack([reshaped])

        result=model.predict(reshaped)

 

        

        label=np.argmax(result,axis=1)[0]

      

        cv2.rectangle(im,(x,y),(x+w,y+h),GR_dict[label],2)

        cv2.rectangle(im,(x,y-40),(x+w,y),GR_dict[label],-1)

        cv2.putText(im, results[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)

 

    cv2.imshow('LIVE',   im)

    key = cv2.waitKey(10)

    

    if key == 27: 

        break

 

cap.release()

 

cv2.destroyAllWindows()

 

Summary

 

In this project, we have developed a deep learning model for face mask detection using Python, Keras, and OpenCV. We developed the face mask detector model for detecting whether person is wearing a mask or not. We have trained the model using Keras with network architecture. 

Now we will test the results of face mask detector model using OpenCV.

 

 

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