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Webcam-App/app.py

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+import os
+import sys
+from cv2 import cv2
+import json
+import numpy as np
+import tensorflow as tf
+
+MODEL_PATH =os.path.join(os.getcwd(),'mask_classifier.h5')
+MODEL_PATH2 =os.path.join(os.getcwd(),'mask.json')
+
+json_file=open(MODEL_PATH2,'r')
+loaded_model_json=json_file.read()
+json_file.close()
+
+model=tf.keras.models.model_from_json(loaded_model_json)
+model.load_weights(MODEL_PATH)
+
+cascPath="haarcascade_frontalface_default.xml"
+faceCascade=cv2.CascadeClassifier(cascPath)
+video_capture=cv2.VideoCapture(0)
+video_capture.set(cv2.CAP_PROP_FRAME_WIDTH,640)
+video_capture.set(cv2.CAP_PROP_FRAME_HEIGHT,480)
+
+
+while True:
+  ret,frame=video_capture.read()
+  if frame is not None:
+    gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
+
+    image=cv2.resize(frame,(64,64))
+    image=np.array(image,dtype='float32')/255.0
+    image=np.expand_dims(image,axis=0)
+    pred=model.predict(image)
+    #pred=np.amax(pred,axis=1)
+    print(pred)
+    if np.argmax(pred)==1:
+        text='no mask'
+    else:
+        text='mask'
+
+    faces=faceCascade.detectMultiScale(
+        gray,
+        scaleFactor=1.1,
+        minNeighbors=5,
+        minSize=(30,30),
+        #flags=cv2.CV_HAAR_SCALE_IMAGE  
+    )
+
+    for (x,y,w,h) in faces:
+        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
+
+    cv2.putText(frame , text , (x , y) , cv2.FONT_HERSHEY_SIMPLEX , 0.45 , (255,255,255), 2)
+    cv2.imshow('Video', frame)
+
+  if cv2.waitKey(1) & 0xFF == ord('q'):
+    break
+
+# When everything is done, release the capture
+video_capture.release()
+cv2.destroyAllWindows() 
+
+

Webcam-App/video.py.py

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+from cv2 import cv2
+import os
+import numpy as np
+import tensorflow as tf
+import json
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+from tensorflow.keras.preprocessing.image import img_to_array
+
+video = cv2.VideoCapture(0)
+path_model=os.path.join(os.getcwd(),'res10_300x300_ssd_iter_140000.caffemodel')
+path_protxt=os.path.join(os.getcwd(),'deploy.prototxt.txt')
+video.set(cv2.CAP_PROP_FRAME_WIDTH,640)
+video.set(cv2.CAP_PROP_FRAME_HEIGHT,480)
+net = cv2.dnn.readNetFromCaffe(path_protxt ,path_model)
+
+MODEL_PATH =os.path.join(os.getcwd(),'model3.h5')
+MODEL_PATH2 =os.path.join(os.getcwd(),'model3.json')
+
+json_file=open(MODEL_PATH2,'r')
+loaded_model_json=json_file.read()
+json_file.close()
+
+model=tf.keras.models.model_from_json(loaded_model_json)
+model.load_weights(MODEL_PATH)
+
+conf = 0.70
+no_conf = 1.99
+
+while True:
+	check, frame = video.read()
+	blob = cv2.dnn.blobFromImage(cv2.resize(frame,(300,300)), 1.0 ,(300,300) ,(104.0, 177.0, 123.0))
+	(h,w) = frame.shape[:2]
+	counter=0
+	net.setInput(blob)
+	detections = net.forward()
+
+	for i2 in range(0 , detections.shape[2]):
+		confidence = detections[0,0,i2,2]
+
+		if (confidence > conf) and (confidence < no_conf) :
+			box = detections[0,0,i2,3:7]*np.array([w,h,w,h])
+			(startX , startY , endX , endY) = box.astype("int")
+			(startX, startY) = (max(0, startX),max(0, startY))
+			(endX, endY) = min(w - 1, endX), min(h - 1, endY)
+
+			face = frame[startY:endY, startX:endX]
+			face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
+			face = cv2.resize(face, (224, 224))
+			face = img_to_array(face)
+			face = preprocess_input(face)
+			face = np.expand_dims(face, axis=0)
+
+			(mask, withoutMask) = model.predict(face)[0]
+			print(mask,withoutMask)
+			if mask < withoutMask:
+				label="Mask" 
+				color=(0,255,0)  
+			else :
+				label="No Mask"
+				color=(0,0,255)
+
+
+			label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
+			cv2.rectangle(frame , (startX , startY) , (endX,endY) , color , 2)
+			cv2.putText(frame , label , (startX , startY) , cv2.FONT_HERSHEY_SIMPLEX , 0.45 , (255,255,255), 2)
+
+	cv2.imshow('Video window', frame)
+
+	# press 'q' on keyboard to exit
+	if cv2.waitKey(1) & 0xFF == ord('q'):
+		break
+
+video.release()
+cv2.destroyAllWindows()