text: 
  from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
  from tensorflow.keras.preprocessing.image import img_to_array
  from tensorflow.keras.models import load_model
  from imutils.video import VideoStream
  import numpy as np
  import argparse
  import imutils
  import time
  import cv2
  import os
  
  def detect_and_predict_mask(frame, faceNet, maskNet):
  	(h, w) = frame.shape[:2]
  	blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
  		(104.0, 177.0, 123.0))
  	faceNet.setInput(blob)
  	detections = faceNet.forward()
  	faces = []
  	locs = []
  	preds = []
  	for i in range(0, detections.shape[2]):
  		confidence = detections[0, 0, i, 2]
  		if confidence > args["confidence"]:
  			box = detections[0, 0, i, 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)
  			faces.append(face)
  			locs.append((startX, startY, endX, endY))
  
  	if len(faces) > 0:
  		faces = np.array(faces, dtype="float32")
  		preds = maskNet.predict(faces, batch_size=32)
  
  	return (locs, preds)
  ap = argparse.ArgumentParser()
  ap.add_argument("-f", "--face", type=str,
  	default="face_detector",
  	help="path to face detector model directory")
  ap.add_argument("-m", "--model", type=str,
  	default="a.h5",
  	help="path to trained face mask detector model")
  ap.add_argument("-c", "--confidence", type=float, default=0.5,
  	help="minimum probability to filter weak detections")
  args = vars(ap.parse_args())
  
  print("[INFO] loading face detector model...")
  prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
  weightsPath = os.path.sep.join([args["face"],
  	"res10_300x300_ssd_iter_140000.caffemodel"])
  faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
  
  print("[INFO] loading face mask detector model...")
  maskNet = load_model(args["model"])
  
  print("[INFO] starting video stream...")
  vs = VideoStream(src=0).start()
  time.sleep(2.0)
  while True:
  	frame = vs.read()
  	frame = imutils.resize(frame, width=400)
  	(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
  	for (box, pred) in zip(locs, preds):
  		(startX, startY, endX, endY) = box
  		(mask, withoutMask) = pred
  
  		label = "Mask" if mask > withoutMask else "No Mask"
  		color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
  
  
  		label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
  
  
  		cv2.putText(frame, label, (startX, startY - 10),
  			cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
  		cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
  
  
  	cv2.imshow("Frame", frame)
  	key = cv2.waitKey(1) & 0xFF
  
  	if key == ord("q"):
  		break
  
  cv2.destroyAllWindows()
  vs.stop()