#!/usr/bin/env python
"""This programm converts an image into an adaptiveThreshold one.

Usage:
  adaptive_threshold -i <source> -o <output> [-s <int>] [-b <int>] [-n]
  adaptive_threshold -h | --help

Options:
  -i <source> --input <source>      Path to input image
  -o <target> --output <target>     Path to output image
  -b <int> --brightness <int>       Change brightness of the output image
  -s <int> --size <int>             Size of local area
  -n --invert                       Flip black/white
  -h --help                         Show this screen.
"""
from docopt import docopt
import numpy as np
import cv2 as cv

def apply(img, size=21, brightness=2):
    size = size or 21
    brightness = brightness or 2
    return cv.adaptiveThreshold(img, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, int(size), int(brightness))

# face_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')
# faces = face_cascade.detectMultiScale(img, 1.3, 5)
# for (x,y,w,h) in faces:
#     # To draw a rectangle in a face
#     #cv.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)
#     roi_gray = img[y:y+h, x:x+w]
#     thresh = roi_gray.mean()
#     # img[x:x+w,y:y+h]
#
#     ret,th1 = cv.threshold(img, thresh*1.2, 255,cv.THRESH_BINARY)

def main():
    arguments = docopt(__doc__)
    img = cv.imread(arguments["--input"],0)
    out = apply(img,arguments["--size"],arguments["--brightness"])
    if arguments["--invert"]:
        out = np.abs(out==0)*255
    cv.imwrite(arguments["--output"], out)

if __name__ == '__main__':
    main()