The interface call opencv face detection (simple)

CV2 Import                 
Import matplotlib.pyplot AS PLT                        
 % matplotlib inline                                
 
# extracted pre-trained face detection model, download advance good model 
face_cascade = cv2.CascadeClassifier ( ' haarcascades / haarcascade_frontalface_alt.xml ' ) 
 
# loading color (channel order BGR) image 
IMG = cv2.imread ( ' images / 9f510fb30f2442a70a9add3dd143ad4bd0130295.jpg ' ) 
 
# BGR the image gradation processing 
gray = cv2.cvtColor (IMG, cv2.COLOR_BGR2GRAY) 
 
# find a face in an image 
faces = face_cascade.detectMultiScale (gray) 
 
# the number of the printed image of the detected face 
print (' Number The of Faces Detected: ' , len (Faces)) 
 
Print (type (Faces)) 
 
# obtain each of the detected face recognition block 
for (X, Y, W, H) in Faces: 
    # the facial image map out the identification frame 
    cv2.rectangle (IMG, (X, Y), (X + W, Y + H), ( 255 , 0 , 0 ), 2 ) 
    
# BGR images into the RGB image to print 
CV_RGB = CV2 .cvtColor (IMG, cv2.COLOR_BGR2RGB) 
 
# display image frame containing the recognition 
plt.imshow (CV_RGB) 
plt.show ()

OpenCV the  Haar feature-based cascade classifiers  to detect faces in the image. OpenCV provides a number of pre-trained face detection model, they are saved in the XML file  github