OneEye Changeset - 1ceee1fdf122

Changeset - 1ceee1fdf122

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Laman - 6 years ago 2019-02-08 13:31:14

computing and visualising grid parameters

1 file changed with 64 insertions and 13 deletions:

exp/hough.py

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exp/hough.py

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@@ @@ -4,57 +4,68 @@ sys.path.append("../src") @@
 import os
 import math
 from datetime import datetime
 import logging as log
 import numpy as np
 import scipy.optimize
 import cv2 as cv
 from annotations import DataFile,computeBoundingBox
 from analyzer.epoint import EPoint
 class HoughTransform:
 	def __init__(self,img):
 		self._angleBandwidth=30 # degrees
 		(h,w)=img.shape[:2]
-		diagLen=np.sqrt(h**2+w**2)
+		self._diagLen=int(np.sqrt(h**2+w**2))+1
 		self._center=(w//2,h//2)
-		self._acc=np.zeros((360,int(diagLen//2)+1),dtype=np.int32)
+		self._acc=np.zeros((180,self._diagLen),dtype=np.int32)
 		self.update(img)
 	def extract(self):
 		maxVal=self._acc.max()
 		arr=np.expand_dims(np.uint8(255*self._acc//maxVal),axis=2)
 		img=np.concatenate((arr,arr,arr),axis=2)
 		shift=self._diagLen//2
 		peaks=sorted(list(findPeaks(self._acc)),key=lambda rc: self._acc[rc],reverse=True)[:2*19]
 		peaks=[(alpha,d-shift) for (alpha,d) in peaks]
 		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")
 		(alpha,beta)=self._detectDominantAngles(peaks)
 		img=self._createImg()
 		doublePeaks=peaks+[(alpha+180,-d) for (alpha,d) in peaks]
 		params=self._computeGridParams([(gamma,d+shift) for (gamma,d) in doublePeaks if alpha<=gamma<=alpha+self._angleBandwidth])
 		self._drawGridCurve(img,params,0)
 		params=self._computeGridParams([(gamma,d+shift) for (gamma,d) in doublePeaks if beta<=gamma<=beta+self._angleBandwidth])
 		self._drawGridCurve(img,params,1)
 		self.show(img)
 	def update(self,img,weight=1):
 		start=datetime.now().timestamp()
 		for (r,row) in enumerate(img):
 			for (c,pix) in enumerate(row):
 				if pix==0: continue
 				for alphaDeg in range(0,360):
 					d=self._computeDist(c,r,alphaDeg)
 					if d>=0: self._acc[(alphaDeg,d)]+=weight
 				for alphaDeg in range(0,180):
 					d=self._computeDist(c,r,alphaDeg)+self._diagLen//2
 					self._acc[(alphaDeg,d)]+=weight
 		log.debug("Hough updated in %s s",round(datetime.now().timestamp()-start,3))
 	def _computeDist(self,x,y,alphaDeg):
 		alphaRad=alphaDeg*math.pi/180
 		(x0,y0)=self._center
 		(dx,dy)=(x-x0,y-y0)
 		d=dx*math.cos(alphaRad)+dy*math.sin(alphaRad)
-		return int(d)
+		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=10
 		center=EPoint(*self._center)
 		res=[]
 		for (alphaDeg,d) in peaks:
 			alphaRad=alphaDeg*math.pi/180
 			point=EPoint.fromPolar((alphaRad,d),center)
@@ @@ -67,13 +78,13 @@ class HoughTransform: @@
 			if ctrl: res.append((alphaDeg,d))
 		return res
 	def _detectDominantAngles(self,peaks):
 		angles=[alpha for (alpha,d) in peaks]
 		n=len(angles)
-		bandwidth=30
+		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
@@ @@ -84,12 +95,52 @@ class HoughTransform: @@
 		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 _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()
 		show(img,"Hough transform accumulator")
 	def _createImg(self):
 		maxVal=self._acc.max()
 		arr=np.expand_dims(np.uint8(255*self._acc//maxVal),axis=2)
 		img=np.concatenate((arr,arr,arr),axis=2)
 		(h,w)=img.shape[:2]
 		for x in range(0,w,4): # y axis
 			img[h//2,x]=[255,255,255]
 		for y in range(0,h,4):
 			img[y,w//2]=[255,255,255]
 		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=int(curve(x))
 			if y<0 or y>=2*h: continue
 			if y<h:	img[y,x]=color
 			else: img[y%h,-x]=color
 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):
 		for c in range(w):

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