#!/usr/bin/python
import Image,sys, numpy as np

def mk2d(l, n):
    for i in xrange(0, len(l), n):
        yield l[i:i+n]
tresh = lambda img,tr: img.point(lambda x: 255 if x>tr else 0)

def vsplit(img, t=1, smdrop=0, tr2=125):
	i2=list(mk2d(list(img.getdata()), img.size[0]))
	x,y=img.size
	cols=[ [i2[r][c] for r in range(y)] for c in range(x)]
	colcnt=[sum([1 if r>tr2 else 0 for r in c]) for c in cols]
	xb=0
	xe=0
	ret=[]
	print 'ffsd', len(colcnt), x
	while xe<x-1:
		while colcnt[xb]<t and xb <x: xb+=1
		xe=xb
		while colcnt[xe]>=t and xe<x-1: xe+=1
		if xe-xb>smdrop:	#found
			ret.append(img.crop((xb,0,xe,y)))
			ret[-1].load()
			ret[-1]=ret[-1].copy()	#dla pewnosci
		print xb,xe
		xb=xe
	return ret
	
#nieuzywane
def otsu2(img):
	hist = np.array(img.histogram())
	hist = hist.astype(np.double)
	Ng = len(hist)
	nB = np.cumsum(hist)
	nO = nB[-1]-nB
	mu_B = 0
	mu_O = (np.arange(1, Ng)*hist[1:]).sum()/hist[1:].sum()
	best = nB[0]*nO[0]*(mu_B-mu_O)*(mu_B-mu_O)
	bestT = 0
	for T in xrange(1, Ng):
		if nB[T] == 0: continue
		if nO[T] == 0: break
		mu_B = (mu_B*nB[T-1] + T*hist[T]) / nB[T]
		mu_O = (mu_O*nO[T-1] - T*hist[T]) / nO[T]
		sigma_between = nB[T]*nO[T]*(mu_B-mu_O)*(mu_B-mu_O)
		if sigma_between > best:
			best = sigma_between
			bestT = T
	return bestT

def main(argv):	
	i=Image.open(argv[1])
	prefix=""
	if len(argv) <3: prefix=""
	else: prefix=argv[2]
	s1=tresh(i.convert('L'),170).convert('1')
	l2=vsplit(s1, 1)
	if len(l2) < 4:
		print "ERR"
		return 2
	for i in range(4):
		l2[i].save(prefix+str(i)+'.png')
		
	
if __name__ == '__main__':
	main(sys.argv)