OneEye Changeset - 6d4447a2e050

Changeset - 6d4447a2e050

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Laman - 6 years ago 2019-02-07 11:27:25

filtering close points, detecting dominant angles

1 file changed with 47 insertions and 3 deletions:

exp/hough.py

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exp/hough.py

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 import sys
 sys.path.append("../src")
 import os
 import sys
 import math
 from datetime import datetime
 import logging as log
@@ @@ -8,6 +10,7 @@ import numpy as np @@
 import cv2 as cv
 from annotations import DataFile,computeBoundingBox
 from analyzer.epoint import EPoint
 class HoughTransform:
@@ @@ -23,7 +26,12 @@ class HoughTransform: @@
 		maxVal=self._acc.max()
 		arr=np.expand_dims(np.uint8(255*self._acc//maxVal),axis=2)
 		img=np.concatenate((arr,arr,arr),axis=2)
 		log.debug(sorted(list(findPeaks(self._acc)),key=lambda rc: self._acc[rc],reverse=True)[:2*19])
 		peaks=sorted(list(findPeaks(self._acc)),key=lambda rc: self._acc[rc],reverse=True)[:2*19]
 		peaks=self._filterClose(peaks)
 		peaks=[(alpha,d) if alpha<180 else (alpha-180,-d) for (alpha,d) in peaks]
 		peaks.sort(key=lambda rc: rc[0])
 		log.debug("detected peaks: %s",peaks)
 		self._detectDominantAngles(peaks)
 		show(img,"Hough transform accumulator")
 	def update(self,img,weight=1):
@@ @@ -43,8 +51,44 @@ class HoughTransform: @@
 		d=dx*math.cos(alphaRad)+dy*math.sin(alphaRad)
 		return int(d)
 	def _filterClose(self,peaks): # a naive implementation
 		minDist=10
 		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 findPeaks(arr2d): # naive implementation
 	def _detectDominantAngles(self,peaks):
 		angles=[alpha for (alpha,d) in peaks]
 		n=len(angles)
 		bandwidth=30
 		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 165>abs(alpha[0]-beta[0])>15]
 		beta=dominantAngles[0]
 		log.debug("dominant angles: %s, %s",alpha,beta)
 		return (alpha[0],beta[0])
 def findPeaks(arr2d): # a naive implementation
 	(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):

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