# -*- coding: UTF-8 -*-
import numpy as np
from scipy import ndimage

def find_miny(mat,val = 1):
    (n,m) = mat.shape
    for i in range(0,n):
        for j in range(0,m):
            if mat[i,j] == val:
                return i

    return None

def find_minx(mat,val = 1):
    (n,m) = mat.shape
    for j in range(0,m):
        for i in range(0,n):
            if mat[i,j] == val:
                return j

    return None


def find_maxy(mat,val = 1):
    (n,m) = mat.shape
    for i in range(n-1,-1,-1):
        for j in range(m-1,-1,-1):
            if mat[i,j] == val:
                return i

    return None

def find_maxx(mat,val = 1):
    (n,m) = mat.shape
    for j in range(m-1,-1,-1):
        for i in range(n-1,-1,-1):
            if mat[i,j] == val:
                return j

    return None


def calc_image_crop(img, val = 255):
    minx = find_minx(img,val)
    maxx = find_maxx(img,val)
    miny = find_miny(img,val)
    maxy = find_maxy(img,val)
    return (minx,maxx,miny,maxy)

def crop_image(img,val = 255):
    (minx,maxx,miny,maxy) = calc_image_crop(img,val)
    return img[miny:maxy,minx:maxx]

def crop_image_prefilled(img,val = 255):
    (minx,maxx,miny,maxy) = calc_image_crop(img,val)
    img_filled = ndimage.binary_fill_holes(img)
    return img_filled[miny:maxy,minx:maxx]

def dimension_rate(img):
    (n,m) = img.shape
    return float(m)/float(n)

def image_to_mask(img,threshold = 128):
    m = img >= threshold
    return 1*m