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)