Locate the center of the face within the Region of Interest

I want to receive a notification when my face's center enters a specific region of interest (ROI). Can this be detected using openCV? Here is the code I have been working on:

import cv2
import sys
import numpy as np

cascPath = 'haarcascade_frontalface_default.xml'
faceCascade = cv2.CascadeClassifier(cascPath)
video_capture = cv2.VideoCapture(0)

while True:
    ret, frame = video_capture.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    roiLeft = frame[0:0, 200:480]
    roiRight = frame[640:0, 440:480]
    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.2,
        minNeighbors=1,
        minSize=(120, 120),
        flags=cv2.cv.CV_HAAR_SCALE_IMAGE
    )

    for (x, y, w, h) in faces:
        centerFrame = x+w/2,y+h/2
        cv2.circle(frame,(x+w/2,y+h/2),10,(0,0,255),-1)
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
        if centerFrame in frame[640:0, 440:480]:
            print("Notification")

    cv2.imshow('Video', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

video_capture.release()
cv2.destroyAllWindows()

Answer â„–1

Here is a suggested solution:

1: Calculate the average minimum and maximum HSV values of your face color. 2: Apply a threshold to the Region of Interest (ROI) using the

cv2.inRange(hsv, COLOR_MIN, COLOR_MAX)
function.

3: If the image contains HSV values within the specified range, that part of the image will be displayed as white; otherwise, it will be displayed as black.

For reference, see this example: https://i.stack.imgur.com/ABCD.png

4: Determine the contour area of the identified region. For facial recognition purposes, an area of at least 8000 or greater is recommended. If the contour area matches or exceeds this value, it is likely to be your face; otherwise, it is not.

You may also find useful information in this resource, particularly regarding Global Thresholding and extracting specific colors.

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