Face & Eye Detection using HAAR Cascade Classifiers



In [12]:

    
import numpy as np
import cv2

# We point OpenCV's CascadeClassifier function to where our 
# classifier (XML file format) is stored
face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')

# Load our image then convert it to grayscale
image = cv2.imread('images/Trump.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Our classifier returns the ROI of the detected face as a tuple
# It stores the top left coordinate and the bottom right coordiantes
faces = face_classifier.detectMultiScale(gray, 1.3, 5)

# When no faces detected, face_classifier returns and empty tuple
if faces is ():
    print("No faces found")

# We iterate through our faces array and draw a rectangle
# over each face in faces
for (x,y,w,h) in faces:
    cv2.rectangle(image, (x,y), (x+w,y+h), (127,0,255), 2)
    cv2.imshow('Face Detection', image)
    cv2.waitKey(0)
    
cv2.destroyAllWindows()

Let's combine face and eye detection



In [7]:

    
import numpy as np
import cv2
 
face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')
eye_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_eye.xml')
 
img = cv2.imread('images/Trump.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

faces = face_classifier.detectMultiScale(gray, 1.3, 5)

# When no faces detected, face_classifier returns and empty tuple
if faces is ():
    print("No Face Found")

for (x,y,w,h) in faces:
    cv2.rectangle(img,(x,y),(x+w,y+h),(127,0,255),2)
    cv2.imshow('img',img)
    cv2.waitKey(0)
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = img[y:y+h, x:x+w]
    eyes = eye_classifier.detectMultiScale(roi_gray)
    for (ex,ey,ew,eh) in eyes:
        cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(255,255,0),2)
        cv2.imshow('img',img)
        cv2.waitKey(0)
    
cv2.destroyAllWindows()

Let's make a live face & eye detection, keeping the face inview at all times



In [11]:

    
import cv2
import numpy as np

face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')
eye_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_eye.xml')

def face_detector(img, size=0.5):
    # Convert image to grayscale
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
    if faces is ():
        return img
    
    for (x,y,w,h) in faces:
        x = x - 50
        w = w + 50
        y = y - 50
        h = h + 50
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = img[y:y+h, x:x+w]
        eyes = eye_classifier.detectMultiScale(roi_gray)
        
        for (ex,ey,ew,eh) in eyes:
            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,0,255),2) 
            
    roi_color = cv2.flip(roi_color,1)
    return roi_color

cap = cv2.VideoCapture(0)

while True:

    ret, frame = cap.read()
    cv2.imshow('Our Face Extractor', face_detector(frame))
    if cv2.waitKey(1) == 13: #13 is the Enter Key
        break
        
cap.release()
cv2.destroyAllWindows()

Tuning Cascade Classifiers

ourClassifier.detectMultiScale(input image, Scale Factor , Min Neighbors)

Scale Factor Specifies how much we reduce the image size each time we scale. E.g. in face detection we typically use 1.3. This means we reduce the image by 30% each time it’s scaled. Smaller values, like 1.05 will take longer to compute, but will increase the rate of detection.

Min Neighbors Specifies the number of neighbors each potential window should have in order to consider it a positive detection. Typically set between 3-6. It acts as sensitivity setting, low values will sometimes detect multiples faces over a single face. High values will ensure less false positives, but you may miss some faces.



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