mask detection program

[Aldiano0]

excuse me, sorry in advance I want to ask you about the mask detection program below, how can we connect this mask detection program with the servo control program so that when the detection is wearing a mask the servo will move to open the door. maybe you can help me
Thank you

USAGE

python detect_mask_video.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 argparse
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, (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]
		if face.any():
			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)

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(