Project.py

import cv2
# variables
# distance from camera to object(face) measured
Known_distance = 30 #centimeter
# width of face in the real world or Object Plane
Known_width =14.3
# Colors
GREEN = (0, 255, 0)
RED = (0, 0, 255)
WHITE = (255, 255, 255)
fonts = cv2.FONT_HERSHEY_COMPLEX
cap = cv2.VideoCapture(0) #Use camera 0
# face detector object
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# focal length finder function
def FocalLength(measured_distance, real_width, width_in_rf_image):
    focal_length = (width_in_rf_image* measured_distance)/ real_width
    return focal_length
# distance estimation function
def Distance_finder (Focal_Length, real_face_width, face_width_in_frame):
    distance = (real_face_width * Focal_Length)/face_width_in_frame
    return distance

def face_data(image):
    face_width = 0
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces = face_detector.detectMultiScale(gray_image, 1.3, 5)
    for (x, y, h, w) in faces:
        cv2.rectangle(image, (x, y), (x+w, y+h), WHITE, 1)
        face_width = w

    return face_width


while True:
    _, frame = cap.read()

    # calling face_data function
    face_width_in_frame = face_data(frame)
    # finding the distance by calling function Distance finder
    if face_width_in_frame != 0:
        Distance = Distance_finder(Focal_length_found, Known_width,face_width_in_frame)
    # Drwaing Text on the screen
        cv2.putText(frame, f"Distance = {Distance}", (50,50), fonts,1, (WHITE),2)
    cv2.imshow("frame", frame )
    if cv2.waitKey(1) == ord("q"):
        break
cap.release()
cv2.destroyAllWindows()