DetecMask.py

# 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

#import serial

import time 

#Setting up your arduino

#arduino = serial.Serial('/dev/ttyUSB0',9600)

lowConfidence = 0.75

#face detectinon function

def detectAndPredictMask(frame, faceNet, maskNet):

	(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()

	# 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 > lowConfidence:

			# 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, 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:

		faces = np.array(faces, dtype="float32")

		preds = maskNet.predict(faces, batch_size=32)

	# return a 2-tuple of the face locations and their corresponding

	return (locs, preds)

# load our serialized face detector model from disk

prototxtPath = r"deploy.prototxt"

weightsPath = r"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

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 900 pixels

	frame = vs.read()

	frame = imutils.resize(frame, width=900)

	# detect faces in the frame and determine if they are wearing a face mask or not

	(locs, preds) = detectAndPredictMask(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)

		if label =="Mask":

			print("ACCESS GRANTED")

			#arduino.write(b'H')

		else: 

			print("ACCESS DENIED")

			#arduino.write(b'L')

		# 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("FaceMask Detection by KAREM -- q to quit", 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()