如何使用面部识别在 opencv 上获得人脸的侧视图？

Question

I tried to use Haar cascades called haarcascade_profileface.xml and lbpcascade_profileface.xml together but the camera does not even open at all. How can I fix this issue where I want both haar cascades to work? This is done on the raspberry pi and can also run on Linux and windows as well. Please explain as best as possible: Here is the code:

import numpy as np    

import cv2    

import time    

import RPi.GPIO as GPIO    

GPIO.setmode(GPIO.BCM)    
GPIO.setwarnings(False)    
GPIO.setup(18,GPIO.OUT)    

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

side_face_cascade = cv2.CascadeClassifier('lbpcascade_frontalface_improved.xml')    

prevTime = 0    

## This will get our web camera     

cap = cv2.VideoCapture(0)    

font = cv2.FONT_HERSHEY_SIMPLEX    

while True:    

retval, frame = cap.read()    

if not retval:    
    
break    

_, img = cap.read()              ## This gets each frame from the video, cap.read returns 2 variables flag - indicate frame is correct and 2nd is f    


##img = cv2.imread('Z.png') Then we get our image we want to use    

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)   # This method only works on gray skin images, so we have to convert the gray scale to rgb image    


faces = face_cascade.detectMultiScale(gray, 1.1, 5) ## Next, we detect the faces    

    if len(faces) > 0:    
        print("[INFO] found {0} faces!".format(len(faces)))    
        GPIO.output(18,GPIO.HIGH)    
    else:    
        print("No face")    
        GPIO.output(18,GPIO.LOW)    
    curTime = time.time()    
    sec = curTime - prevTime    
    prevTime = curTime    
    fps = 1/(sec)    
    str = "FPS : %0.1f" % fps     
    for (x, y, w, h) in faces:   ## We draw a rectangle around the faces so we can see it correctly    
        cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0))         ## The faces will be a list of coordinates    
        cv2.putText(img, 'Myface', (x, y), font, fontScale=1, color=(255,70,120),thickness=2)
        side_faces = side_face_cascade.detectMultiScale(gray, 1.1, 5)
        for (ex, ey, ew, eh) in side_faces:   ## We draw a rectangle around the faces so we can see it correctly    
             cv2.rectangle(img, (ex, ey), (ex+ew, ey+eh), (255, 0, 0))         ## The faces will be a list of coordinates    
             cv2.putText(img, 'Myface', (ex, ey), font, fontScale=1, color=(255,70,120),thickness=2)    
    cv2.putText(frame, 'Number of Faces Detected: ' + str, (0,  100), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0))    
    cv2.imshow('img', img) ## Last we show the image    
    x = cv2.waitKey(30) & 0xff    
    if x==27:    
        break    
## Press escape to exit the program    
cap.release()    

Answer 1

OpenCV actually provides a "side-face" detector. It is called 'haarcascade_profileface.xml'. You can do:

side_face_cascade = cv2.CascadeClassifier('haarcascade_profileface.xml')
side_faces = side_face_cascade.detectMultiScale(gray, 1.1, 5)