starting cv2 and face recognition.

import cv2

 cap=cv2.VideoCapture(0)

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

while True:

    ret,frame=cap.read()

    gray_frame=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)

    if ret == False:

        continue

        

    faces=face_cascade.detectMultiScale(gray_frame,1.3,5)  

    for (x,y,w,h) in faces:

        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)

    cv2.imshow("VideoFrame",frame)

    

    

    key_pressed = cv2.waitKey(1) & 0xFF

    if key_pressed == ord('q'):

       break

        

cap.release()

cv2.destroyAllWindows()

******************************designing opencv logo****************************

import cv2

import numpy as np

#angles are measured clockwise

img_=np.zeros((591,591,3))

c_c=(300,180)

axes_len=(80,80)

angle = 45

startAngle = 90

endAngle = 360

color = (255,0,0)

thickness = 10

cv2.ellipse(img_,c_c,axes_len,angle,startAngle,endAngle,color,thickness)

cv2.ellipse(img_,(180,361),axes_len,50,0,270,(0,255,0),10)

cv2.ellipse(img_,(420,361),axes_len,145,90,360,(0,0,255),10)

font = cv2.FONT_HERSHEY_SIMPLEX

cv2.putText(img_,'OpenCV',(50,550), font, 4,(255,255,255),2,cv2.LINE_AA)

cv2.imshow('image',img_)

cv2.waitKey()

cv2.destroyAllWindows()

       **********************************eyes_classifier**********************

import cv2

import numpy as np

import matplotlib.pyplot as plt

img = cv2.imread('my_photo.jpg')

fix_img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)

eye_classifier = cv2.CascadeClassifier('haarcascade_eyes.xml')

def detect_eyes(fix_img):

    eyes_rect = eye_classifier.detectMultiScale(fix_img)

    

    for (x,y,w,h) in eyes_rect:

        cv2.rectangle(fix_img,(x,y),(x+w,y+h),(0,0,0),4)

        

    return fix_img

result = detect_eyes(fix_img)

plt.imshow(result)