A face mask detector in Python

 # 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()