detect.py

import logging
import cv2
import numpy
import glob, os

faceClassifier = cv2.CascadeClassifier()
faceClassifier.load('haarcascade_frontalface_default.xml')

imageProbes = []
typeProbes = []
trainShape = (100,110)

def fillFaces(facesList,typesList, directory, typeOfFace):
    for img in glob.glob(directory):
        print "Loading ", img
        try:
            i = cv2.imread(img, cv2.IMREAD_GRAYSCALE)
            faces = faceClassifier.detectMultiScale(i,    
                    scaleFactor=1.1,
                    minNeighbors=5,
                    minSize=(30, 30),
                    flags = cv2.cv.CV_HAAR_SCALE_IMAGE)

            for face in faces:
                faceImage = i[face[1]:face[1]+face[3], face[0]:face[0]+face[2]]
                faceImage = cv2.resize(faceImage, trainShape)
                imageProbes.append(numpy.asarray(faceImage, dtype=numpy.uint8))
                typeProbes.append(typeOfFace)
        except Exception as er:
            print er

fillFaces(imageProbes, typeProbes, "y/*", 1)
fillFaces(imageProbes, typeProbes, "n/*", 0)

recognizer = cv2.createFisherFaceRecognizer()
recognizer.train(
        numpy.asarray(imageProbes), 
        numpy.asarray(typeProbes))

for img in glob.glob("t/*"):
    print "Testing ", img
    i = cv2.imread(img, cv2.IMREAD_GRAYSCALE)
    faces = faceClassifier.detectMultiScale(i,    
            scaleFactor=1.1,
            minNeighbors=5,
            minSize=(30, 30),
            flags = cv2.cv.CV_HAAR_SCALE_IMAGE)

    for face in faces:
        faceImage = i[face[1]:face[1]+face[3], face[0]:face[0]+face[2]]
        faceImage = cv2.resize(faceImage, trainShape)
        label, confidence = recognizer.predict(faceImage)
        print label, confidence
        cv2.imshow('image',faceImage)
        cv2.waitKey(0)
        cv2.destroyAllWindows()