WGU-Capstone/Face_Detect with borders.py

import cv2
import numpy as np


multiplication_factor = 0.05

def get_adjustment_amount(imgSize, currentX, currentY, currentW, currentH):

    current_top_left = [currentX, currentY]
    current_bottom_right = [currentX + currentW, currentY + currentH]

    current_top_right = [currentX + currentW, currentY]

    # find the difference between the left gap and the right gap, divide it by two, and multiply it by the speed scale
    horizontal_adjustment = multiplication_factor * (currentX - (imgSize[0] - current_top_right[0])) / 2
    vertical_adjustment = multiplication_factor * (currentY - (imgSize[0] - current_bottom_right[1])) / 2

    return [horizontal_adjustment, vertical_adjustment]

cap = cv2.VideoCapture(0, cv2.IMREAD_GRAYSCALE) # instead of grayscale you can also use -1, 0, or 1.
faceCascade = cv2.CascadeClassifier(r"./lbpcascade_frontalface.xml") # CHECK THIS FIRST TROUBLE SHOOTING

tmp, frm = cap.read()
height, width, channels = frm.shape
print(f"{height*.25}, {width}")
del tmp, frm

#Color is 1, grayscale is 0, and the unchanged is -1
while(True):
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
 
    # Detect faces in the image
    faces = faceCascade.detectMultiScale(
    gray,
    scaleFactor=1.1,
    minNeighbors=5,
    minSize=(30, 30)
    )


    # Draw a rectangle around the faces
    for (x, y, w, h) in faces:
        adjustment_required = get_adjustment_amount([width, height], x, y, w, h)
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 255, 255))
        print(f"Adjust right: {adjustment_required[0]}")
        print(f"Adjust up   : {adjustment_required[1]}")
    cv2.imshow('frame', frame)

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

cap.release()