face_cascade = cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml')

eye_cascade = cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_eye.xml')

cv2.destroyAllWindows()

# -*- coding: utf-8 -*-

import numpy as np

import cv2

face_cascade = cv2.CascadeClassifier('/home/play/opencv-2.4.11/data/haarcascades/haarcascade_frontalface_default.xml')

eye_cascade = cv2.CascadeClassifier('/home/play/opencv-2.4.11/data/haarcascades/haarcascade_eye.xml')

img = cv2.imread('../data/sachin.jpg')

# img = cv2.imread('../data/kongjie_hezhao.jpg')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#Detects objects of different sizes in the input image.

# The detected objects are returned as a list of rectangles.

#cv2.CascadeClassifier.detectMultiScale(image, scaleFactor, minNeighbors, flags, minSize, maxSize)

#scaleFactor – Parameter specifying how much the image size is reduced at each image

#scale.

#minNeighbors – Parameter specifying how many neighbors each candidate rectangle should

#have to retain it.

#minSize – Minimum possible object size. Objects smaller than that are ignored.

#maxSize – Maximum possible object size. Objects larger than that are ignored.

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

print "Detected ",len(faces)," face"

for (x,y,w,h) in faces:

img = 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_cascade.detectMultiScale(roi_gray)

for (ex,ey,ew,eh) in eyes:

cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

cv2.imshow('img',img)

cv2.waitKey(0)

cv2.destroyAllWindows()