Rectangle Faces

#!/usr/bin/env python2
# -*- coding: utf-8 -*- #

from twitterbot import TwitterBot
from picbot import PicBot
from PIL import Image, ImageDraw
from sys import argv

import numpy
import cv2

import keys


classifier = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(classifier)


def face_detect(image):
    """
    Return rectangles of identified face regions
    """

    # numpy grayscale image for face detection
    array = numpy.asarray(image)
    gray_image = cv2.cvtColor(array, cv2.COLOR_BGR2GRAY)

    # tweak this for better results ..

    faces = faceCascade.detectMultiScale(
        gray_image,
        scaleFactor=1.1,
        minNeighbors=5,
        minSize=(30, 30),
        flags=cv2.cv.CV_HAAR_SCALE_IMAGE
    )

    # convert boxes from from arrays to tuples
    boxes = [(x, y, x + w, y + h) for (x, y, w, h) in faces]
    return boxes


HORIZONTAL = Image.FLIP_TOP_BOTTOM
VERTICAL = Image.FLIP_LEFT_RIGHT



def face_rectangle(image):
    """
    Make white rectangle over the eyes
    """

    # work on a copy of the image
    image = image.copy()

    # face detection with opencv
    boxes = face_detect(image)

    # get drawing context for the current image
    ctx = ImageDraw.Draw(image)

    # define colors
    white = (255, 255, 255)

    # draw boxes
    for box in boxes:

        # draw a white box around it
        x1, y1, x2, y2 = box
        box = x1 - 1 , y1 + 10, x2 + 1, y2 - 10
        ctx.rectangle(box, fill=white, outline=None)

    return image, boxes


if __name__ == '__main__':

    print("Loading source image")
    src = Image.open("gt.jpg")
    #src.show()

    print("Testing face rectangle")
    img, boxes = face_rectangle(src)
    img.show()