diff --git a/exp/board_detect.py b/exp/board_detect.py
--- a/exp/board_detect.py
+++ b/exp/board_detect.py
@@ -13,7 +13,6 @@ import scipy.cluster
 import scipy.ndimage
 import scipy.signal
 
-from geometry import Line
 from polar_hough import PolarHough
 from annotations import DataFile,computeBoundingBox
 from hough import show,prepareEdgeImg,HoughTransform
@@ -73,21 +72,6 @@ def filterStones(contours,bwImg,stoneDim
 	return res
 
 
-def groupLines(points,minCount,tolerance):
-	random.shuffle(points)
-	sample=points[:]
-	for (i,a) in enumerate(sample):
-		for (j,b) in enumerate(sample):
-			if j<=i: continue
-			ab=Line(a,b)
-			for c in points:
-				if c is a or c is b: continue
-				if point2lineDistance(a,b,c)<=tolerance:
-					ab.points.add(c)
-			if len(ab.points)>=minCount:
-				yield ab
-
-
 class BoardDetector:
 	def __init__(self,annotationsPath):
 		self._annotations=DataFile(annotationsPath)
@@ -130,9 +114,6 @@ class BoardDetector:
 		hough.update(stonesImg,5)
 		hough.extract()
 
-		# # detect lines passing through the stones
-		# lines=self._constructLines(stones)
-		#
 		# # detect vanishing points of the lines
 		# imgCenter=EPoint(w//2-x1, h//2-y1)
 		# (a,b,c,d)=(p-EPoint(x1,y1) for p in self._annotations[filename][0])
@@ -191,44 +172,6 @@ class BoardDetector:
 		show(stones,"black and white areas")
 		return stones
 
-	def _constructLines(self,stoneLocs):
-		lineDict=dict()
-		# minCount=min(max(math.sqrt(len(stoneLocs))-4,3),7)
-		minCount=6
-		log.debug("min count: %s",minCount)
-		points=[point for (point,contour) in stoneLocs]
-		for line in groupLines(points,minCount,2):
-			key=line.getSortedPoints()
-			if key in lineDict: # we already have a line with the same incident points
-				continue
-			lineDict[line.getSortedPoints()]=line
-			obsolete=set()
-			for ab in lineDict.values():
-				if ab is line: continue
-				if line.points<ab.points: # == impossible
-					del lineDict[key]
-					break
-				if ab.points<line.points:
-					obsolete.add(ab.getSortedPoints())
-			for key in obsolete: del lineDict[key]
-		log.debug("valid lines: %s",len(lineDict))
-		lines=sorted(lineDict.values(), key=lambda ab: len(ab.points), reverse=True)
-
-		# visualize
-		linesImg=cv.cvtColor(np.zeros((self._rectH,self._rectW),np.uint8),cv.COLOR_GRAY2BGR)
-		cv.drawContours(linesImg,[c for (point,c) in stoneLocs],-1,(255,255,255),-1)
-		for (p,c) in stoneLocs:
-			cv.drawMarker(linesImg,(int(p.x),int(p.y)),(255,0,0),cv.MARKER_CROSS)
-		self._printLines(lines,points,linesImg)
-		for line in lines:
-			points=line.getSortedPoints()
-			(xa,ya)=points[0]
-			(xb,yb)=points[-1]
-			cv.line(linesImg,(int(xa),int(ya)),(int(xb),int(yb)),(255,255,0),1)
-		show(linesImg)
-
-		return lines
-
 	def _printLines(self,lines,allPoints,img):
 		for (i,line) in enumerate(lines):
 			img_=np.copy(img)
diff --git a/exp/hough.py b/exp/hough.py
--- a/exp/hough.py
+++ b/exp/hough.py
@@ -10,8 +10,6 @@ import scipy.optimize
 import scipy.signal
 import cv2 as cv
 
-from analyzer.epoint import EPoint
-
 DEBUG=True
 
 
@@ -74,45 +72,6 @@ class HoughTransform:
 		d=dx*math.cos(alphaRad)+dy*math.sin(alphaRad)
 		return round(d)
 
-	def _filterClose(self,peaks): # a naive implementation
-		"""Discard points with Euclidean distance on the original image lower than 10.
-		From such pairs keep only the one with a higher value in the accumulator.
-		This can delete a series of points. If a-b and b-c are close and a>b>c, only a is kept."""
-		minDist=13
-		center=EPoint(*self._center)
-		res=[]
-		for (alphaDeg,d) in peaks:
-			alphaRad=alphaDeg*math.pi/180
-			point=EPoint.fromPolar((alphaRad,d),center)
-			ctrl=True
-			for (betaDeg,e) in peaks:
-				betaRad=betaDeg*math.pi/180
-				point_=EPoint.fromPolar((betaRad,e),center)
-				if point.dist(point_)<minDist and self._acc[(alphaDeg,d)]<self._acc[(betaDeg,e)]:
-					ctrl=False
-			if ctrl: res.append((alphaDeg,d))
-		return res
-
-	def _detectDominantAngles(self,peaks):
-		angles=[alpha for (alpha,d) in peaks]
-		n=len(angles)
-		bandwidth=self._angleBandwidth
-		k1=0
-		k2=1
-		histogram=[]
-		while k1<n:
-			while (k2<n and angles[k1]+bandwidth>angles[k2]) or (k2>=n and angles[k1]+bandwidth>angles[k2%n]+180):
-				k2+=1
-			histogram.append((angles[k1],k2-k1))
-			k1+=1
-		log.debug("angles histogram: %s",histogram)
-		dominantAngles=sorted(histogram,key=lambda xy: xy[1],reverse=True)
-		alpha=dominantAngles[0]
-		dominantAngles=[beta for beta in dominantAngles if 180-bandwidth>abs(alpha[0]-beta[0])>bandwidth]
-		beta=dominantAngles[0]
-		log.debug("dominant angles: %s, %s",alpha,beta)
-		return (alpha[0],beta[0])
-
 	def _detectLines(self):
 		bag=LineBag()
 		for alpha in range(0,180,2):
@@ -134,15 +93,6 @@ class HoughTransform:
 			res.append((k,i))
 		return res
 
-	def _computeGridParams(self,lines):
-		log.debug("computing grid parameters for: %s",lines)
-		angles=[alpha for (alpha,d) in lines]
-		dists=[d for (alpha,d) in lines]
-		curve=lambda x,a,b,c,d: a*x**3+b*x**2+c*x+d
-		(params,cov)=scipy.optimize.curve_fit(curve,dists,angles)
-		log.debug("result: %s",params)
-		return params
-
 	def show(self,img=None):
 		if img is None: img=self._createImg()
 
@@ -162,24 +112,6 @@ class HoughTransform:
 
 		return img
 
-	def _markPeaks(self,img,peaks):
-		colors=[[255,0,0],[255,255,0],[0,255,0],[0,255,255],[0,0,255]]
-		for (i,(alpha,d)) in enumerate(peaks[:38]):
-			cv.drawMarker(img,(d,alpha),colors[i//9],cv.MARKER_TILTED_CROSS)
-		return img
-
-	def _drawGridCurve(self,img,params,colorKey=0):
-		colors=[[0,255,255],[255,0,255],[255,255,0]]
-		color=colors[colorKey]
-		(a,b,c,d)=params
-		(h,w)=img.shape[:2]
-		curve=lambda x: a*x**3+b*x**2+c*x+d
-		for x in range(0,w,3):
-			y=round(curve(x))
-			if y<0 or y>=2*h: continue
-			if y<h:	img[y,x]=color
-			else: img[y%h,-x]=color
-
 	def _drawLine(self,img,alpha,beta,peaks,colorKey):
 		colors=[[0,255,255],[255,0,255],[255,255,0]]
 		color=colors[colorKey]
@@ -193,27 +125,6 @@ class HoughTransform:
 			(y,x)=keys[k]
 			cv.drawMarker(img,(x,y),color,cv.MARKER_TILTED_CROSS,8)
 
-	def drawLine(self,img,line,colorKey=0):
-		colors=[[0,255,255],[255,0,255],[255,255,0]]
-		color=colors[colorKey]
-		(h,w)=img.shape[:2]
-		(a,b,c)=line.toNormal()
-		if b==0: return
-		for x in range(1,w,3):
-			y=round((-c-a*x)/b) + (0 if b>=0 else 180)
-			if y<0 or y>=h: continue
-			img[y,x]=color
-
-
-def findPeaks(arr2d): # a naive implementation
-	"""Scan 8-neighbourhood and for each peak or top plateau yield one point. For plateaus yield the """
-	(h,w)=arr2d.shape
-	neighbours=[(-1,-1),(-1,0),(-1,1),(0,-1),(0,1),(1,-1),(1,0),(1,1)]
-	for r in range(h):
-		for c in range(w):
-			if all(r+dr<0 or r+dr>=h or c+dc<0 or c+dc>=w or arr2d[r,c]>arr2d[r+dr,c+dc] or (i<4 and arr2d[r,c]>=arr2d[r+dr,c+dc]) for (i,(dr,dc)) in enumerate(neighbours)):
-				yield (r,c)
-
 
 def show(img,filename="x"):
 	cv.imshow(filename,img)
@@ -256,13 +167,3 @@ def prepareEdgeImg(img):
 	edges=filterHor(edges)+filterVert(edges)+filterDiag(edges)
 	show(edges,"kernel filtered edges")
 	return edges
-
-def houghLines(bwImg):
-	colorImg=cv.cvtColor(bwImg,cv.COLOR_GRAY2BGR)
-	lines = cv.HoughLinesP(bwImg,1,np.pi/180,10,minLineLength=10,maxLineGap=40)
-	if lines is None: lines=[]
-	for line in lines:
-		x1,y1,x2,y2 = line[0]
-		cv.line(colorImg,(x1,y1),(x2,y2),(0,255,0),1)
-
-	show(colorImg)