diff --git a/exp/quantization.py b/exp/quantization.py
new file mode 100644
--- /dev/null
+++ b/exp/quantization.py
@@ -0,0 +1,65 @@
+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)