FaceMask Detector

Author: Divesh2003

facemask.py

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+import numpy as np
+import keras
+import keras.backend as k
+from keras.layers import Conv2D,MaxPooling2D,SpatialDropout2D,Flatten,Dropout,Dense
+from keras.models import Sequential,load_model
+from keras.optimizers import Adam
+from keras.preprocessing import image
+import cv2
+import datetime
+
+
+# UNCOMMENT THE FOLLOWING CODE TO TRAIN THE CNN FROM SCRATCH
+
+# BUILDING MODEL TO CLASSIFY BETWEEN MASK AND NO MASK
+
+model=Sequential()
+model.add(Conv2D(32,(3,3),activation='relu',input_shape=(150,150,3)))
+model.add(MaxPooling2D() )
+model.add(Conv2D(32,(3,3),activation='relu'))
+model.add(MaxPooling2D() )
+model.add(Conv2D(32,(3,3),activation='relu'))
+model.add(MaxPooling2D() )
+model.add(Flatten())
+model.add(Dense(100,activation='relu'))
+model.add(Dense(1,activation='sigmoid'))
+
+model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
+
+from keras.preprocessing.image import ImageDataGenerator
+train_datagen = ImageDataGenerator(
+        rescale=1./255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True)
+
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+training_set = train_datagen.flow_from_directory(
+        'train',
+        target_size=(150,150),
+        batch_size=16 ,
+        class_mode='binary')
+
+test_set = test_datagen.flow_from_directory(
+        'test',
+        target_size=(150,150),
+        batch_size=16,
+        class_mode='binary')
+
+model_saved=model.fit_generator(
+        training_set,
+        epochs=10,
+        validation_data=test_set,
+
+        )
+
+model.save('mymodel.h5',model_saved)
+
+#To test for individual images
+
+mymodel=load_model('mymodel.h5')
+#test_image=image.load_img('C:/Users/Karan/Desktop/ML Datasets/Face Mask Detection/Dataset/test/without_mask/30.jpg',target_size=(150,150,3))
+test_image=image.load_img(r'C:/Users/karan/Desktop/FaceMaskDetector/test/with_mask/1-with-mask.jpg',
+                          target_size=(150,150,3))
+test_image
+test_image=image.img_to_array(test_image)
+test_image=np.expand_dims(test_image,axis=0)
+mymodel.predict(test_image)[0][0]
+
+
+# IMPLEMENTING LIVE DETECTION OF FACE MASK
+
+mymodel=load_model('mymodel.h5')
+
+cap=cv2.VideoCapture(0)
+face_cascade=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+while cap.isOpened():
+    _,img=cap.read()
+    face=face_cascade.detectMultiScale(img,scaleFactor=1.1,minNeighbors=4)
+    for(x,y,w,h) in face:
+        face_img = img[y:y+h, x:x+w]
+        cv2.imwrite('temp.jpg',face_img)
+        test_image=image.load_img('temp.jpg',target_size=(150,150,3))
+        test_image=image.img_to_array(test_image)
+        test_image=np.expand_dims(test_image,axis=0)
+        pred=mymodel.predict(test_image)[0][0]
+        if pred==1:
+            cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),3)
+            cv2.putText(img,'NO MASK',((x+w)//2,y+h+20),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),3)
+        else:
+            cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),3)
+            cv2.putText(img,'MASK',((x+w)//2,y+h+20),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),3)
+        datet=str(datetime.datetime.now())
+        cv2.putText(img,datet,(400,450),cv2.FONT_HERSHEY_SIMPLEX,0.5,(255,255,255),1)
+          
+    cv2.imshow('img',img)
+    
+    if cv2.waitKey(1)==ord('q'):
+        break
+    
+cap.release()
+cv2.destroyAllWindows()