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findextrema.py
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findextrema.py
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from PIL import Image
import scipy
import numpy
import scipy.ndimage
import math
import itertools
import harris
import hessian
import contrast
class findextrema(object):
def getPatextremes(self,ims,pa):
"""
find local extremas on pattern image
"""
# instantiate funtional class
hs = harris.harris()
hess = hessian.hessian()
cont = contrast.contrast()
coordinates = []
temp = {}
H = [0,1,2,3]
W = [0,1,2,3]
for i in range(4):
H[i] = len(ims[i][0])
W[i] = len(ims[i][0][0])
localArea = [0,1,2]
# get the unstable and low contrast pixel
hs_points = hs.corner(pa)
hess_points = hess.Patedgedetect(pa)
low_contrast = cont.lowcontrast(pa)
# compute the pixels which are not situable for pixel matching
bad_points = list(set(hs_points) | set(hess_points) | set(low_contrast))
bad = dict.fromkeys(bad_points, 0)
for m in range(4):
for n in range(1,3):
for i in range(16,H[m]-16):
for j in range(16,W[m]-16):
if bad.has_key((i*2**m,j*2**m))==False :
# compare local pixel with its 26 neighbour
currentPixel = ims[m][n][i][j]
localArea[0] = ims[m][n-1][i-1:i+2,j-1:j+2]
localArea[1] = ims[m][n][i-1:i+2,j-1:j+2]
localArea[2] = ims[m][n+1][i-1:i+2,j-1:j+2]
Area = numpy.array(localArea)
maxLocal = numpy.array(Area).max()
minLocal = numpy.array(Area).min()
if (currentPixel == maxLocal) or (currentPixel == minLocal):
if temp.has_key((i*2**m,j*2**m)) == False:
coordinates.append([int(i*2**m),int(j*2**m)])
temp[(i*2**m,j*2**m)] = [i*2**m,j*2**m]
return coordinates
def get_Srcextremes(self,ims,sa):
"""
find local extremas on pattern image
"""
# instantiate funtional class
hs = harris.harris()
hess = hessian.hessian()
cont = contrast.contrast()
coordinates = []
temp = {}
H = [0,1,2,3]
W = [0,1,2,3]
for i in range(4):
H[i] = len(ims[i][0])
W[i] = len(ims[i][0][0])
localArea = [0,1,2]
# get the unstable and low contrast pixel
hs_points = hs.corner(sa)
hess_points = hess.Srcedgedetect(sa)
low_contrast = cont.lowcontrast(sa)
# compute the pixels which are not situable for pixel matching
bad_points = list(set(hs_points) | set(hess_points) | set(low_contrast))
bad = dict.fromkeys(bad_points, 0)
for m in range(4):
for n in range(1,3):
for i in range(16,H[m]-16):
for j in range(16,W[m]-16):
if bad.has_key((i*2**m,j*2**m))==False :
# compare local pixel with its 26 neighbour
currentPixel = ims[m][n][i][j]
localArea[0] = ims[m][n-1][i-1:i+2,j-1:j+2]
localArea[1] = ims[m][n][i-1:i+2,j-1:j+2]
localArea[2] = ims[m][n+1][i-1:i+2,j-1:j+2]
Area = numpy.array(localArea)
maxLocal = numpy.array(Area).max()
minLocal = numpy.array(Area).min()
if (currentPixel == maxLocal) or (currentPixel == minLocal):
if temp.has_key((i*2**m,j*2**m)) == False:
coordinates.append([int(i*2**m),int(j*2**m)])
temp[(i*2**m,j*2**m)] = [i*2**m,j*2**m]
return coordinates