detect_mask_video.py

# USAGE
# python detect_mask_video.py

# importation des modèles nécessaires
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 simpleaudio as sa
import asyncio
import vlc
import numpy as np
import argparse
import imutils
import time
import cv2
import os

def detect_and_predict_mask(frame, faceNet, maskNet):
	# Saisie des dimensions du cadre, puis construction une goutte à partir de celle-ci
	(h, w) = frame.shape[:2]
	#blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size, mean, swapRB=True)
	
	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)

			# 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)

# 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)
wave_obj = sa.WaveObject.from_wave_file("audio/audio.wav")
# 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 800 pixels
	frame = vs.read()
	frame = imutils.resize(frame, width=800)

	# 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
        
		if mask < withoutMask:

			label = "No Mask"
			color = (0, 0, 255)

			smiley = "\pacontent.jpg"

			# smiley integration ---------------------------------------------------------------------------

			# path 
			path = 'image' + smiley
				
			# Reading an image in default mode 
			image = cv2.imread(path) 
				
			# Window name in which image is displayed 
			window_name = 'Image'
			
			# font 
			font = cv2.FONT_HERSHEY_SIMPLEX 
			
			# org 
			org = (50, 50) 
			
			# fontScale 
			fontScale = 1
			
			# Blue color in BGR 
			color_img = (255, 0, 0) 
			
			# Line thickness of 2 px 
			thickness = 2
			
			# Using cv2.putText() method 
			image = cv2.putText(image, '', org, font,  
							fontScale, color_img, thickness, cv2.LINE_AA)

			#End smiley integration ------------------------------------------------------------------------

			# 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)

		else:
			label = "Mask"
			color = (0, 255, 0)

			smiley = '\smiley.jpg'

			# smiley integration ---------------------------------------------------------------------------

			# path 
			path = 'image' + smiley
				
			# Reading an image in default mode 
			image = cv2.imread(path) 
				
			# Window name in which image is displayed 
			window_name = 'Image'
			
			# font 
			font = cv2.FONT_HERSHEY_SIMPLEX 
			
			# org 
			org = (50, 50) 
			
			# fontScale 
			fontScale = 1
			
			# Blue color in BGR 
			color_img = (255, 0, 0) 
			
			# Line thickness of 2 px 
			thickness = 2
			
			# Using cv2.putText() method 
			image = cv2.putText(image, '', org, font,  
							fontScale, color_img, thickness, cv2.LINE_AA)

			#End smiley integration ------------------------------------------------------------------------


			# 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)

	# Displaying the image
	cv2.imshow(window_name, image)
	

	key = cv2.waitKey(1) & 0xFF
	# if the `q` key was pressed, break from the loop
	if key == ord("q"):
		break

	if mask < withoutMask:
		play_obj = wave_obj.play()
		play_obj.wait_done()

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()