model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(100, (3, 3), activation='relu',
                           input_shape=(150, 150, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
  
    tf.keras.layers.Conv2D(100, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2, 2),
  
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(50, activation='relu'),
    tf.keras.layers.Dense(2, activation='softmax')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])

# import the necessary packages
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 imutils
import time
import cv2
import os
  
  
def detect_and_predict_mask(frame, faceNet, maskNet):
    
    # grab the dimensions of the frame and 
    # then construct a blob from it
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),
                                 (104.0, 177.0, 123.0))
  
    # pass the blob through the network 
    # and obtain the face detections
    faceNet.setInput(blob)
    detections = faceNet.forward()
    print(detections.shape)
  
    # initialize our list of faces, their
    # corresponding locations, and the list
    # of predictions from our face mask network
    faces = []
    locs = []
    preds = []
  
    # loop over the detections
    for i in range(0, detections.shape[2]):
        
        # extract the confidence (i.e.,
        # probability) associated with
        # the detection
        confidence = detections[0, 0, i, 2]
  
        # filter out weak detections by 
        # ensuring the confidence is
        # greater than the minimum confidence
        if confidence > 0.5:
            
            # 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")
  
            # ensure the bounding boxes fall 
            # within the dimensions of
            # the frame
            (startX, startY) = (max(0, startX), max(0, startY))
            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))
  
            # extract the face ROI, convert it
            # from BGR to RGB channel
            # ordering, resize it to 224x224, 
            # and preprocess it
            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)
  
            # add the face and bounding boxes 
            # to their respective lists
            faces.append(face)
            locs.append((startX, startY, endX, endY))
  
    # only make a predictions if at least one
    # face was detected
    if len(faces) > 0:
        
        # for faster inference we'll make 
        # batch predictions on *all*
        # faces at the same time rather 
        # than one-by-one predictions
        # in the above `for` loop
        faces = np.array(faces, dtype="float32")
        preds = maskNet.predict(faces, batch_size=32)
  
    # return a 2-tuple of the face locations
    # and their corresponding locations
    return (locs, preds)
  
  
# load our serialized face detector model from disk
prototxtPath = r"face_detector\deploy.prototxt"
weightsPath = r"face_detector\res10_300x300_ssd_iter_140000.caffemodel"
faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
  
# load the face mask detector model from disk
maskNet = load_model("mask_detector.model")
  
# initialize the video stream
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
  
# loop over the frames from the video stream
while True:
    # grab the frame from the threaded 
    # video stream and resize it
    # to have a maximum width of 400 pixels
    frame = vs.read()
    frame = imutils.resize(frame, width=400)
  
    # detect faces in the frame and 
    # determine if they are wearing a
    # face mask or not
    (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
  
    # loop over the detected face 
    # locations and their corresponding
    # locations
    for (box, pred) in zip(locs, preds):
        
        # unpack the bounding box and predictions
        (startX, startY, endX, endY) = box
        (mask, withoutMask) = pred
  
        # determine the class label and 
        # color we'll use to draw
        # the bounding box and text
        label = "Mask" if mask > withoutMask else "No Mask"
        color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
  
        # include the probability in the label
        label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
  
        # display the label and bounding box 
        # rectangle on the output frame
        cv2.putText(frame, label, (startX, startY - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
        cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
  
    # show the output frame
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF
  
    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break
  
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()