OneEye Changeset - 6aace8f39e75

Changeset - 6aace8f39e75

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Laman - 6 years ago 2019-03-04 19:29:53

detecting board diagonals with RANSAC

3 files changed with 86 insertions and 4 deletions:

exp/board_detect.py

exp/hough.py

exp/ransac.py

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exp/board_detect.py

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 import sys
 sys.path.append("../src")
 import os
 import math
 import random
 import itertools
 import logging as log
 import cv2 as cv
 import numpy as np
 import scipy.cluster
 import scipy.ndimage
 import scipy.signal
 from geometry import Line
 from ransac import DiagonalRansac
 from annotations import DataFile,computeBoundingBox
 from hough import show,prepareEdgeImg,HoughTransform
 from analyzer.epoint import EPoint
 from analyzer.corners import Corners
 random.seed(361)
@@ @@ -122,12 +122,22 @@ class BoardDetector: @@
 		# # rectify the image
 		matrix=self._computeTransformationMatrix(lines[0][0],lines[0][-1],lines[1][0],lines[1][-1])
 		transformed=cv.warpPerspective(rect,matrix,(self._rectW,self._rectH))
 		# determine precise board edges
 		intersections=[]
 		for p in lines[0]:
 			for q in lines[1]:
 				intersections.append(p.intersect(q))
 		sack=DiagonalRansac(intersections,19)
 		diagonals=sack.extract(10,2000)
 		log.debug("diagonals candidates: %s",diagonals)
 		for line in diagonals:
 			self._drawLine(linesImg,line,[0,255,255])
 		show(linesImg,"diagonals candidates")
 	def _detectRough(self,img,filename):
 		corners=self._annotations[filename][0]
 		(x1,y1,x2,y2)=computeBoundingBox(corners)
 		log.debug("bounding box: (%s,%s) - (%s,%s)",x1,y1,x2,y2)
 		return (x1,y1,x2,y2)
@@ @@ -188,13 +198,14 @@ class BoardDetector: @@
 		show(rect)
 		transformed=cv.warpPerspective(rect,matrix,(self._rectW,self._rectH))
 		show(transformed,"rectified image")
 		return matrix
 	def _drawLine(self,img,line):
 	def _drawLine(self,img,line,color=None):
 		if not color: color=[0,255,0]
 		(h,w)=img.shape[:2]
 		corners=[EPoint(0,0),EPoint(w,0),EPoint(0,h),EPoint(w,h)] # NW NE SW SE
 		borders=[
 			[Line.fromPoints(corners[0],corners[1]), Line.fromPoints(corners[2],corners[3])], # N S
 			[Line.fromPoints(corners[0],corners[2]), Line.fromPoints(corners[1],corners[3])] # W E
+		]
@@ @@ -204,13 +215,13 @@ class BoardDetector: @@
 		if not a or not b:
 			(a,b)=(line.intersect(borders[1][0]), line.intersect(borders[1][1]))
 			log.debug("* %s %s",line,(a,b))
 		if any(abs(x)>10**5 for x in [*a,*b]):
 			log.debug("ignored")
 			return
-		cv.line(img,(int(a.x),int(a.y)),(int(b.x),int(b.y)),[0,255,0])
+		cv.line(img,(int(a.x),int(a.y)),(int(b.x),int(b.y)),color)
 if __name__=="__main__":
 	detector=BoardDetector(sys.argv[2])
 	filepath=sys.argv[1]
 	filename=os.path.basename(filepath)

exp/hough.py

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@@ @@ -24,12 +24,13 @@ class LineBag: @@
 	def pull(self,count):
 		self._lines.sort(reverse=True)
 		res=[]
 		for (score,alpha,beta,peaks) in self._lines:
 			if any(abs(alpha-gamma)<10 and abs(beta-delta)<10 for (_,gamma,delta,_) in res): continue
 			# avoid intersecting lines
 			if any((beta-delta)!=0 and (alpha-gamma)/(beta-delta)<0 for (_,gamma,delta,_) in res): continue
 			res.append((score,alpha,beta,peaks))
 			if len(res)>=count: break
 		return res
@@ @@ -62,13 +63,13 @@ class HoughTransform: @@
 			res.append([])
 			keys=self._readLineKeys(alpha,beta)
 			for k in peaks:
 				(alphaDeg,d)=keys[k]
 				line=Line(alphaDeg*math.pi/180,d-self._diagLen//2)
 				res[-1].append(self._transformOutput(line))
 			res[-1].sort(key=lambda line: line.d)
+			res[-1].sort(key=lambda line: line.d if line.alpha<math.pi else -line.d)
 			i+=1
 		self.show(img)
 		return res
 	def update(self,img,weight=1):

exp/ransac.py

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@@ new file 100644 @@
 import random
 from geometry import Line
 class LineBag:
 	def __init__(self,capacity):
 		self._capacity=capacity
 		self._bag=[]
 	def put(self,item):
 		# early abort
 		if self._bag and item>self._bag[-1]: return
 		(v,p)=item
 		# mostly filter duplicates
 		for (i,(vi,pi)) in enumerate(self._bag):
 			if abs(p.alpha-pi.alpha)<0.02 and abs(p.d-pi.d)<5:
 				if v<vi: self._bag[i]=item
 				return
 		# add new and remove the last
 		self._bag.append(item)
 		self._bag.sort()
 		if len(self._bag)>self._capacity:
 			self._bag.pop()
 	def pull(self,count=0):
 		return self._bag[:count] if count else self._bag[:]
 class Ransac:
 	def __init__(self,points):
 		self._points=points
 	def extract(self,count,iterations):
 		bag=LineBag(count)
 		for i in range(iterations):
 			line=self._generateLine()
 			score=self._scoreLine(line)
 			bag.put((score+random.random()/1000,line))
 		return [line for (score,line) in bag.pull()]
 	def _generateLine(self):
 		(a,b)=random.sample(self._points,2)
 		return Line.fromPoints(a,b)
 	def _scoreLine(self,line):
 		return sum(min(line.distanceTo(p),5) for p in self._points)
 class DiagonalRansac(Ransac):
 	def __init__(self,points,sideLen):
 		super().__init__(points)
 		self._sideLen=sideLen
 	def _generateLine(self):
 		n=len(self._points)
 		m=self._sideLen
 		ka=random.randrange(0,n)
 		kb=ka
 		while ka//m==kb//m or ka%m==kb%m:
 			kb=random.randrange(0,n)
 		a=self._points[ka]
 		b=self._points[kb]
 		return Line.fromPoints(a,b)

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