import logging as log

import numpy as np
import scipy.cluster
import cv2 as cv


def kmeans(img):
	arr=np.reshape(img,(-1,3)).astype(np.float)
	wood=[193,165,116]
	(centers,distortion)=scipy.cluster.vq.kmeans(arr,3)
	log.debug("k-means centers: %s",centers)
	(black,empty,white)=sorted(centers,key=sum)
	if np.linalg.norm(black)>np.linalg.norm(black-wood):
		black=None
	if np.linalg.norm(white-[255,255,255])>np.linalg.norm(white-wood):
		white=None
	log.debug("black, white: %s, %s",black,white)
	return (black,white,centers)


class QuantizedImage:
	BLACK=0
	WHITE=1
	EMPTY=2

	def __init__(self,img):
		self.img=self._quantize(img)
		self._mask()

	def transform(self,matrix):
		(h,w)=self.img.shape[:2]
		self.img=cv.warpPerspective(self.img,matrix,(w,h))
		self.maskB=cv.warpPerspective(self.maskB,matrix,(w,h))
		self.maskW=cv.warpPerspective(self.maskW,matrix,(w,h))

	def get(self,x,y):
		if self.maskB[y,x]: return self.BLACK
		elif self.maskW[y,x]: return self.WHITE
		else: return self.EMPTY

	def _quantize(self,img):
		(self._black,self._white,colors)=self._sampleColors(img)
		origShape=img.shape
		data=np.reshape(img,(-1,3))
		(keys,dists)=scipy.cluster.vq.vq(data,colors)
		pixels=np.array([colors[k] for k in keys],dtype=np.uint8).reshape(origShape)
		return pixels

	def _sampleColors(self,rect):
		(h,w)=rect.shape[:2]
		minirect=rect[h//4:3*h//4, w//4:3*w//4]
		return kmeans(minirect)

	def _mask(self):
		unit=np.array([1,1,1],dtype=np.uint8)
		if self._black is not None:
			self.maskB=cv.inRange(self.img,self._black-unit,self._black+unit)
		else:
			self.maskB=np.zeros(self.img.shape[:2],dtype=np.uint8)

		if self._white is not None:
			self.maskW=cv.inRange(self.img,self._white-unit,self._white+unit)
		else:
			self.maskW=np.zeros(self.img.shape[:2],dtype=np.uint8)