Face mask Detection Alert System.py

#!/usr/bin/env python
# coding: utf-8

# ### Data Preprocessing

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import cv2
import tensorflow as tf
import numpy as np


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from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split


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img_rows , img_cols = 112 , 112


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import os


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data_dir = os.listdir('Dataset')


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data_dir


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images = []
labels = []


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for category in data_dir:
    folder_path = os.path.join('Dataset' , category)
    
    for img in os.listdir(folder_path):
        img_path = os.path.join(folder_path , img)
        img = cv2.imread(img_path)
        
        try:
            #converting grayscale image
            grayscale_img = cv2.cvtColor(img , cv2.COLOR_BGR2GRAY)
            
            # resize grayscale image to our defined rows and cols
            resized_img = cv2.resize(grayscale_img , (img_rows , img_cols))
            
            images.append(resized_img)
            labels.append(category)
        
        except Exception as e:
            
            print('Exception :' , e)


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len(images)


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len(labels)


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import numpy as np


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images = np.array(images)/255.0
images = np.reshape(images , (images.shape[0] , img_rows , img_cols , 1))


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print(images.shape) # image data is now ready.


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# now we have to encode the labels


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lb = LabelBinarizer()
labels = lb.fit_transform(labels)


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labels = to_categorical(labels)


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labels = np.array(labels)
print(labels)


# ### CNN Model Creation

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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Conv2D,MaxPooling2D,Flatten,Dropout


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model = Sequential()

# First layer
model.add(Conv2D(32 , (3,3) , input_shape=(img_rows, img_cols , 1) , activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))


# Second layer
model.add(Conv2D(64 , (3,3) , activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

# Flatten and Third Layer
model.add(Flatten())
model.add(Dropout(0.5))

#Fourth Layer
model.add(Dense(64 , activation='relu'))
model.add(Dense(2 , activation='softmax'))


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model.summary()


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model.compile(optimizer='adam'  , loss='categorical_crossentropy' , metrics=['accuracy'])


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X_train , X_test , y_train , y_test = train_test_split(images , labels , test_size = 0.25 , random_state = 0)


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history = model.fit(X_train , y_train , epochs=6 , validation_split=0.2)


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model.evaluate(X_test , y_test)


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import seaborn as sns
sns.set()
import matplotlib.pyplot as plt

# %matplotlib notebook


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# plot loss

plt.plot(history.history['val_loss'] , label = 'validation_loss')
plt.plot(history.history['loss'] , label = 'training_loss')
plt.xlabel('Number of epochs')
plt.ylabel('loss')
plt.legend()
plt.show()


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# plot Accuracy


plt.plot(history.history['val_accuracy'] , label = 'validation_accuracy')
plt.plot(history.history['accuracy'] , label = 'training_accuracy')
plt.xlabel('Number of epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()


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model.save('Face_mask_detection.h5')


# ### Load Model

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from tensorflow.keras.models import load_model


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get_ipython().run_line_magic('load_ext', 'nb_black')


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model = load_model('Face_mask_detection.h5')


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import tkinter
import smtplib
from tkinter import messagebox
import cv2
import numpy as np


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root = tkinter.Tk()
root.withdraw()


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face_det_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')


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vid_source = cv2.VideoCapture(0)


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text_dict = {0:'Mask ON' , 1:'NO Mask'}
rect_color_dict = {0:(0,255,0) , 1:(0,0,255)}


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SUBJECT = "Access Denied"   
TEXT = "One Visitor violated Face Mask Policy. See in the camera to recognize user. A Person has been detected without a face mask in this area. Please Alert the authorities."


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while(True):

    ret, img = vid_source.read()
    grayscale_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    faces = face_det_classifier.detectMultiScale(grayscale_img,1.3,5)  

    for (x,y,w,h) in faces:
    
        face_img = grayscale_img[y:y+w,x:x+w]
        resized_img = cv2.resize(face_img,(112,112))
        normalized_img = resized_img/255.0
        reshaped_img = np.reshape(normalized_img,(1,112,112,1))
        result=model.predict(reshaped_img)

        label=np.argmax(result,axis=1)[0]
      
        cv2.rectangle(img,(x,y),(x+w,y+h),rect_color_dict[label],2)
        cv2.rectangle(img,(x,y-40),(x+w,y),rect_color_dict[label],-1)
        cv2.putText(img, text_dict[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(0,0,0),2) 
        
        # If label = 1 then it means wearing No Mask and 0 means wearing Mask
        if (label == 1):
            # Throw a Warning Message to tell user to wear a mask if not wearing one. This will stay
            #open and No Access will be given He/She wears the mask
            messagebox.showwarning("Warning","Access Denied. Please wear a Face Mask")
            
            # Send an email to the administrator if access denied/user not wearing face mask 
            message = 'Subject: {}\n\n{}'.format(SUBJECT, TEXT)
            mail = smtplib.SMTP('smtp.gmail.com', 587)
            mail.ehlo()
            mail.starttls()
             
            # when person not wear a mask information send to this particular image
            
            mail.login('your mail','your mail password') 
            mail.sendmail('mail','mail id password',message)
            mail.close
        else:
            pass
            break

    cv2.imshow('LIVE Video Feed',img)
    key=cv2.waitKey(1)
    
    if(key==27):
        break
        
cv2.destroyAllWindows()
source.release()


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