PyFeatureTrack/pyramid.py at master · TimSC/PyFeatureTrack · GitHub

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#*********************************************************************
#* pyramid.py
#*
#*********************************************************************

fromerrorimport*
fromconvolveimport*
importnumpyasnp

#*********************************************************************
#*
#*

classKLTPyramid:
def__init__(self, ncols, nrows, subsampling, nlevels):

ifsubsampling!=2andsubsampling!=4and \
subsampling!=8andsubsampling!=16andsubsampling!=32:
KLTError("(_KLTCreatePyramid) Pyramid's subsampling must "+ \
"be either 2, 4, 8, 16, or 32")

# Set parameters
self.subsampling=subsampling;
self.nLevels=nlevels;
self.img= []
self.ncols= []
self.nrows= []

# Allocate memory for each level of pyramid and assign pointers
foriinrange(nlevels):
self.img.append(None)
self.ncols.append(ncols)
self.nrows.append(nrows)
ncols/=subsampling
nrows/=subsampling

defCompute(self, img, sigma_fact):

nrows=img.shape[0]
ncols=img.shape[1]
subsampling=self.subsampling
subhalf=subsampling/2
sigma=subsampling*sigma_fact; # empirically determined
#int oldncols;
#int i, x, y;

ifsubsampling!=2andsubsampling!=4and \
subsampling!=8andsubsampling!=16andsubsampling!=32:
KLTError("(_KLTComputePyramid) Pyramid's subsampling must "+ \
"be either 2, 4, 8, 16, or 32")

assertself.ncols[0] ==ncols
assertself.nrows[0] ==nrows

# Copy original image to level 0 of pyramid
self.img[0] =img

currimg=img
foriinrange(1,self.nLevels):
tmpimg=KLTComputeSmoothedImage(currimg, sigma)
#tmpimgL = tmpimg.load()

# Subsample
oldncols=ncols
ncols=int(ncols/subsampling)
nrows=int(nrows/subsampling)
#subsampImg = Image.new("F",(ncols,nrows))
subsampImg=np.empty((nrows,ncols), np.float32)
#subsampImgL = subsampImg.load()
foryinrange(nrows):
forxinrange(ncols):
subsampImg[y,x] =tmpimg[int(subsampling*y+subhalf), int(subsampling*x+subhalf)]

self.img[i] =subsampImg

# Reassign current image
currimg=self.img[i]