detect_mask_video.py

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 argparse
import imutils
import time
import cv2
import os

def detect_and_predict_mask(frame, faceNet, maskNet):

	(h, w) = frame.shape[:2]
	blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
		(104.0, 177.0, 123.0))

	faceNet.setInput(blob)
	detections = faceNet.forward()


	faces = []
	locs = []
	preds = []


	for i in range(0, detections.shape[2]):

		confidence = detections[0, 0, i, 2]


		if confidence > args["confidence"]:

			box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
			(startX, startY, endX, endY) = box.astype("int")

			(startX, startY) = (max(0, startX), max(0, startY))
			(endX, endY) = (min(w - 1, endX), min(h -1, endY))

			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)


			faces.append(face)
			locs.append((startX, startY, endX, endY))

	if len(faces) > 0:

		preds = maskNet.predict(faces)


	return (locs, preds)


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())

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)


print("[INFO] loading face mask detector model...")
maskNet = load_model(args["model"])

print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)


while True:

	frame = vs.read()
	frame = imutils.resize(frame, width=400)

	(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)

	for (box, pred) in zip(locs, preds):

		(startX, startY, endX, endY) = box
		(mask, withoutMask) = pred


		label = "Mask" if mask > withoutMask else "No Mask"
		color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

		label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)


		cv2.putText(frame, label, (startX, startY -10), 
			cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
		cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)

	cv2.imshow("frame", frame)
	key = cv2.waitKey(1) & 0XFF

	if key == ord("q"):
		break

cv2.destroyAllWindows()
vs.stop()