diff --git a/exp/kerokero/prepare_data.py b/exp/kerokero/prepare_data.py
--- a/exp/kerokero/prepare_data.py
+++ b/exp/kerokero/prepare_data.py
@@ -1,6 +1,7 @@
 import os
 import sys
 import re
+import math
 import random
 import logging as log
 
@@ -48,6 +49,19 @@ class Sample:
 		grid=list(map(lambda p: list(2*p/self.SIDE-EPoint(1,1)), grid))
 		return (img,grid)
 
+	def rectify(self):
+		x1=self.SIDE*0.1
+		x2=self.SIDE*0.9
+		abcd=list(map(list,self.grid))
+		destPoints=[(x1,x1),(x1,x2),(x2,x2),(x2,x1)]
+		abcd_=list(map(list, (EPoint(x,y)+self._createNoise() for (x,y) in destPoints)))
+		m=cv.getPerspectiveTransform(np.float32(abcd),np.float32(abcd_))
+		img=cv.warpPerspective(self.img,m,(self.SIDE,self.SIDE))
+		img=np.uint8(img)
+		grid=Corners(c.transform(m) for c in self.grid)
+		grid=list(map(lambda p: list(2*p/self.SIDE-EPoint(1,1)), grid))
+		return (img,grid)
+
 	def _getCenter(self):
 		(a,b,c,d)=self.grid
 		p=Line.fromPoints(a,c)
@@ -63,6 +77,11 @@ class Sample:
 		scale=getScale(self.SIDE/(wg*(1+left+right)), self.SIDE/(hg*(1+top+bottom)))
 		return np.matmul(scale,t2)
 
+	def _createNoise(self):
+		alpha=random.uniform(0,math.pi*2)
+		d=random.uniform(0,self.SIDE*0.05)
+		return EPoint(math.cos(alpha)*d, math.sin(alpha)*d)
+
 	def show(self):
 		img=cv.cvtColor(self.img,cv.COLOR_GRAY2BGR)
 		for c in self.grid:
@@ -91,14 +110,15 @@ def harvestDir(path):
 	for f in files:
 		grade=annotations.get(f.name,[Board()])[0].grade
 		Stats.counts[grade]+=1
-		if not Board.UNSET<grade<=Board.POOR: continue
+		if not Board.UNSET<grade<=Board.GOOD: continue
 		img=cv.imread(f.path)
 		img=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
 		for b in boards:
 			sample=Sample(img,b.grid)
 			# sample.show()
-			(transformedImg,label)=sample.transform()
-			# Sample(np.uint8((transformedImg+1)*128),map(lambda c: (c+EPoint(1,1))*Sample.SIDE/2,transformedGrid)).show()
+			# (transformedImg,label)=sample.transform()
+			(transformedImg,label)=sample.rectify()
+			# Sample(np.uint8(transformedImg),map(lambda c: (EPoint(*c)+EPoint(1,1))*Sample.SIDE/2,label)).show()
 			yield (transformedImg,label)