detect_mask_live_alarm.py

import numpy as np
import argparse
import face_recognition
import imutils
import time
import cv2
import os
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
from pygame import mixer

mixer.init()
sound = mixer.Sound('alarm.WAV')

def run_facecrop():
    # Open the input movie file
    input_movie = cv2.VideoCapture(2)
    
    al_image = face_recognition.load_image_file("without-mask.jpg")
    al_face_encoding = face_recognition.face_encodings(al_image)[0]
    
    known_faces = [
        al_face_encoding
    ]
    
    # Initialize some variables
    face_locations = []
    face_encodings = []
    face_names = []
    frame_number = 0
    
    current_path = os.getcwd()
    
    counter = 0
    
    while True:
        # Grab a single frame of video
        ret, frame = input_movie.read()
        frame_number += 1
    
        # Quit when the input video file ends
        if not ret:
            break
    
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(frame)
        face_encodings = face_recognition.face_encodings(frame, face_locations)
    
        face_names = []
        for face_encoding in face_encodings:
            # See if the face is a match for the known face(s)
            match = face_recognition.compare_faces(known_faces, face_encoding, tolerance=0.50)
    
            name = None
            if match[0]:
                name = "without-mask"
            
            face_names.append(name)
    
        # Label the results
        for (top, right, bottom, left), name in zip(face_locations, face_names):
            if not name:
                continue
    
            # Draw a box around the face
            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
    
            crop_img = frame[top:bottom, left:right]
            if (name == "without-mask"):
                cv2.imwrite(current_path + "/Face_Database/without-mask/" + "without-mask" + str(counter) + ".png", crop_img)
                counter = counter + 1
                
            # Draw a label with a name below the face
            cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
            cv2.putText(frame, 'FACE-MASK NOT ON', (50,40), cv2.FONT_HERSHEY_DUPLEX, 1.1, (0,0,255), 3)

    
        cv2.imshow('Face Crop', frame)
        
        # Hit 'q' on the keyboard to quit!
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    
    input_movie.release()
    cv2.destroyAllWindows()

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, (300, 300),
        (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 > args["confidence"]:
            # 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)
            face = np.expand_dims(face, axis=0)

            # 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:
        
        preds = maskNet.predict(faces)

    # return a 2-tuple of the face locations and their corresponding
    # locations
    return (locs, preds)

# construct the argument parser and parse the arguments
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="mask_detector.model",
    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())

# load our serialized face detector model from disk
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)

# load the face mask detector model from disk
print("[INFO] loading face mask detector model...")
maskNet = load_model(args["model"])

# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)

# loop over the frames from the video stream
while True:
    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) = 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
        if mask > withoutMask:
            cv2.putText(frame, 'FACE-MASK ON', (150,40), cv2.FONT_HERSHEY_DUPLEX, 1.1, (0,0,255), 3)
            print("Mask ON")
        else:
            if mask < withoutMask:
                sound.play()
                cv2.putText(frame, 'FACE-MASK NOT ON', (150,40), cv2.FONT_HERSHEY_DUPLEX, 1.1, (0,0,255), 3)
                print("Mask NOT ON")
                run_facecrop()
            else:
                cv2.putText(frame, 'FACE-MASK ON', (350,40), cv2.FONT_HERSHEY_DUPLEX, 1.1, (0,0,255), 3)
                print("Mask ON")
            
        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()