Now that we understand how to detect faces, we can generalize the concept to detect other body parts too. It's important to understand that Viola-Jones framework can be applied to any object. The accuracy and robustness will depend on the uniqueness of the object. For example, a human face has very unique characteristics, so it's easy to train our system to be robust. On the other hand, an object like towel is too generic, and there are no distinguishing characteristics as such; so it's more difficult to build a robust towel detector.
Let's see how to build an eye detector:
import cv2
import numpy as np
face_cascade = cv2.CascadeClassifier('./cascade_files/haarcascade_frontalface_alt.xml')
eye_cascade = cv2.CascadeClassifier('./cascade_files/haarcascade_eye.xml')
if face_cascade.empty():
raise IOError('Unable to load the face cascade classifier xml file')
if eye_cascade.empty():
raise IOError('Unable to load the eye cascade classifier xml file')
cap = cv2.VideoCapture(0)
ds_factor = 0.5
while True:
ret, frame = cap.read()
frame = cv2.resize(frame, None, fx=ds_factor, fy=ds_factor, interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
roi_gray = gray[y:y+h, x:x+w]
roi_color = frame[y:y+h, x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (x_eye,y_eye,w_eye,h_eye) in eyes:
center = (int(x_eye + 0.5*w_eye), int(y_eye + 0.5*h_eye))
radius = int(0.3 * (w_eye + h_eye))
color = (0, 255, 0)
thickness = 3
cv2.circle(roi_color, center, radius, color, thickness)
cv2.imshow('Eye Detector', frame)
c = cv2.waitKey(1)
if c == 27:
break
cap.release()
cv2.destroyAllWindows()If you run this program, the output will look something like the following image:
If you notice, the program looks very similar to the face detection program. Along with loading the face detection cascade classifier, we load the eye detection cascade classifier as well. Technically, we don't need to use the face detector. But we know that eyes are always on somebody's face. We use this information and search for eyes only in the relevant region of interest, that is the face. We first detect the face, and then run the eye detector on this sub-image. This way, it's faster and more efficient.