text: from imutils.video import VideoStream
  import numpy as np
  import imutils
  import time
  import cv2
  
  confidence_score = 0.5
  prototxt = "Data/deploy.prototxt"
  model = "Data/weights.caffemodel"
  net = cv2.dnn.readNetFromCaffe(prototxt, model)
  cap = cv2.VideoCapture(0)
  time.sleep(2)
  
  while True:
      ret, frame = cap.read()
      #frame = cv2.resize(frame, (400, 400))
      #print(frame)
      (h, w) = frame.shape[:2]
      blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
  
      net.setInput(blob)
      detections = net.forward()
      
      for i in range(0, detections.shape[2]):
  		# extract the confidence (i.e., probability) associated with the prediction
          confidence = detections[0, 0, i, 2]
          
          if confidence < confidence_score:
              continue
   
  		# compute the (x, y)-coordinates of the bounding box for the object
          box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
          (startX, startY, endX, endY) = box.astype("int")
          
          # draw the bounding box of the face along with the associated probability
          text = "{:.2f}%".format(confidence * 100)
          y = startY - 10 if startY - 10 > 10 else startY + 10
          cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 0, 255), 2)
          cv2.putText(frame, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
          
      cv2.imshow("Frame", frame)
      
      if cv2.waitKey(20) & 0xFF == ord("q"):
          break
  
  cap.release()
  cv2.destroyAllWindows()