def processImage(self, imgdata):
stackedname = os.path.join(self.params['rundir'],
imgdata['filename'] + "power.jpg")
if os.path.isfile(stackedname) and apFile.fileSize(stackedname) > 100:
return
### make the power spectra
powerspectra = imagefun.power(imgdata['image'],
mask_radius=1.0,
thresh=4)
binpowerspectra = imagefun.bin2(powerspectra, self.params['bin'])
del powerspectra
if self.params['hp'] is True:
binpowerspectra = apImage.fermiHighPassFilter(binpowerspectra,
apix=4.0,
radius=2000.0)
binpowerspectra = apImage.normStdevMed(binpowerspectra, size=5)
binpowerspectra = apImage.pixelLimitFilter(binpowerspectra, pixlimit=4)
binpowerspectra = apImage.normRange(binpowerspectra)
### filter the image
imagedata = imagefun.bin2(imgdata['image'], self.params['bin'])
del imgdata['image']
imagedata = apImage.normStdevMed(imagedata, size=5)
imagedata = apImage.pixelLimitFilter(imagedata, pixlimit=2)
imagedata = apImage.normRange(imagedata)
### write to file
stacked = numpy.hstack([imagedata, binpowerspectra])
del imagedata, binpowerspectra
apImage.arrayToJpeg(stacked, stackedname)
def processImage(self, imgdata): stackedname = os.path.join(self.params['rundir'], imgdata['filename']+"power.jpg") if os.path.isfile(stackedname) and apFile.fileSize(stackedname) > 100: return ### make the power spectra powerspectra = imagefun.power(imgdata['image'], mask_radius=1.0, thresh=4) binpowerspectra = imagefun.bin2(powerspectra, self.params['bin']) del powerspectra if self.params['hp'] is True: binpowerspectra = apImage.fermiHighPassFilter(binpowerspectra, apix=4.0, radius=2000.0) binpowerspectra = apImage.normStdevMed(binpowerspectra, size=5) binpowerspectra = apImage.pixelLimitFilter(binpowerspectra, pixlimit=4) binpowerspectra = apImage.normRange(binpowerspectra) ### filter the image imagedata = imagefun.bin2(imgdata['image'], self.params['bin']) del imgdata['image'] imagedata = apImage.normStdevMed(imagedata, size=5) imagedata = apImage.pixelLimitFilter(imagedata, pixlimit=2) imagedata = apImage.normRange(imagedata) ### write to file stacked = numpy.hstack([imagedata, binpowerspectra]) del imagedata, binpowerspectra apImage.arrayToJpeg(stacked, stackedname)
def getTiltedRotateShift(img1, img2, tiltdiff, angle=0, bin=1, msg=True):
"""
takes two images tilted
with respect to one another
and tries to find overlap
img1 (as numpy array)
img2 (as numpy array)
tiltdiff (in degrees)
negative, img1 is more compressed (tilted)
positive, img2 is more compressed (tilted)
"""
### untilt images by stretching and compressing
# choose angle s/t compressFactor = 1/stretchFactor
# this only works if one image is untilted (RCT) of both images are opposite tilt (OTR)
#halftilt = abs(tiltdiff)/2.0
halftiltrad = math.acos(math.sqrt(math.cos(abs(tiltdiff)/180.0*math.pi)))
# go from zero tilt to half tilt
compressFactor = math.cos(halftiltrad)
# go from max tilt to half tilt
stretchFactor = math.cos(halftiltrad) / math.cos(abs(tiltdiff)/180.0*math.pi)
if tiltdiff > 0:
if msg is True:
apDisplay.printMsg("compress image 1")
untilt1 = transformImage(img1, compressFactor, angle)
untilt2 = transformImage(img2, stretchFactor, angle)
xfactor = compressFactor
else:
if msg is True:
apDisplay.printMsg("stretch image 1")
untilt1 = transformImage(img1, stretchFactor, angle)
untilt2 = transformImage(img2, compressFactor, angle)
xfactor = stretchFactor
### filtering was done earlier
filt1 = untilt1
filt2 = untilt2
if filt1.shape != filt2.shape:
newshape = ( max(filt1.shape[0],filt2.shape[0]), max(filt1.shape[1],filt2.shape[1]) )
apDisplay.printMsg("Resizing images to: "+str(newshape))
filt1 = apImage.frame_constant(filt1, newshape, filt1.mean())
filt2 = apImage.frame_constant(filt2, newshape, filt2.mean())
### cross-correlate
cc = correlator.cross_correlate(filt1, filt2, pad=True)
rad = min(cc.shape)/20.0
cc = apImage.highPassFilter(cc, radius=rad)
cc = apImage.normRange(cc)
cc = blackEdges(cc)
cc = apImage.normRange(cc)
cc = blackEdges(cc)
cc = apImage.normRange(cc)
cc = apImage.lowPassFilter(cc, radius=10.0)
#find peak
peakdict = peakfinder.findSubpixelPeak(cc, lpf=0)
#import pprint
#pprint.pprint(peak)
pixpeak = peakdict['subpixel peak']
if msg is True:
apDisplay.printMsg("Pixel peak: "+str(pixpeak))
apImage.arrayToJpegPlusPeak(cc, "guess-cross-ang"+str(abs(angle))+".jpg", pixpeak)
rawpeak = numpy.array([pixpeak[1], pixpeak[0]]) #swap coord
shift = numpy.asarray(correlator.wrap_coord(rawpeak, cc.shape))*bin
if msg is True:
apDisplay.printMsg("Found xy-shift btw two images"
+";\n\t SNR= "+str(round(peakdict['snr'],2))
+";\n\t halftilt= "+str(round(halftiltrad*180/math.pi, 3))
+";\n\t compressFactor= "+str(round(compressFactor, 3))
+";\n\t stretchFactor= "+str(round(stretchFactor, 3))
+";\n\t xFactor= "+str(round(xfactor, 3))
+";\n\t rawpeak= "+str(numpy.around(rawpeak*bin, 1))
+";\n\t shift= "+str(numpy.around(shift, 1))
)
return shift, xfactor, peakdict['snr']
def getTiltedRotateShift(img1, img2, tiltdiff, angle=0, bin=1, msg=True):
"""
takes two images tilted
with respect to one another
and tries to find overlap
img1 (as numpy array)
img2 (as numpy array)
tiltdiff (in degrees)
negative, img1 is more compressed (tilted)
positive, img2 is more compressed (tilted)
"""
### untilt images by stretching and compressing
# choose angle s/t compressFactor = 1/stretchFactor
# this only works if one image is untilted (RCT) of both images are opposite tilt (OTR)
#halftilt = abs(tiltdiff)/2.0
halftiltrad = math.acos(math.sqrt(math.cos(abs(tiltdiff)/180.0*math.pi)))
# go from zero tilt to half tilt
compressFactor = math.cos(halftiltrad)
# go from max tilt to half tilt
stretchFactor = math.cos(halftiltrad) / math.cos(abs(tiltdiff)/180.0*math.pi)
if tiltdiff > 0:
if msg is True:
apDisplay.printMsg("compress image 1")
untilt1 = transformImage(img1, compressFactor, angle)
untilt2 = transformImage(img2, stretchFactor, angle)
xfactor = compressFactor
else:
if msg is True:
apDisplay.printMsg("stretch image 1")
untilt1 = transformImage(img1, stretchFactor, angle)
untilt2 = transformImage(img2, compressFactor, angle)
xfactor = stretchFactor
### filtering was done earlier
filt1 = untilt1
filt2 = untilt2
if filt1.shape != filt2.shape:
newshape = ( max(filt1.shape[0],filt2.shape[0]), max(filt1.shape[1],filt2.shape[1]) )
apDisplay.printMsg("Resizing images to: "+str(newshape))
filt1 = apImage.frame_constant(filt1, newshape, filt1.mean())
filt2 = apImage.frame_constant(filt2, newshape, filt2.mean())
### cross-correlate
cc = correlator.cross_correlate(filt1, filt2, pad=True)
rad = min(cc.shape)/20.0
cc = apImage.highPassFilter(cc, radius=rad)
cc = apImage.normRange(cc)
cc = blackEdges(cc)
cc = apImage.normRange(cc)
cc = blackEdges(cc)
cc = apImage.normRange(cc)
cc = apImage.lowPassFilter(cc, radius=10.0)
#find peak
peakdict = peakfinder.findSubpixelPeak(cc, lpf=0)
#import pprint
#pprint.pprint(peak)
pixpeak = peakdict['subpixel peak']
if msg is True:
apDisplay.printMsg("Pixel peak: "+str(pixpeak))
apImage.arrayToJpegPlusPeak(cc, "guess-cross-ang"+str(abs(angle))+".jpg", pixpeak)
rawpeak = numpy.array([pixpeak[1], pixpeak[0]]) #swap coord
shift = numpy.asarray(correlator.wrap_coord(rawpeak, cc.shape))*bin
if msg is True:
apDisplay.printMsg("Found xy-shift btw two images"
+";\n\t SNR= "+str(round(peakdict['snr'],2))
+";\n\t halftilt= "+str(round(halftiltrad*180/math.pi, 3))
+";\n\t compressFactor= "+str(round(compressFactor, 3))
+";\n\t stretchFactor= "+str(round(stretchFactor, 3))
+";\n\t xFactor= "+str(round(xfactor, 3))
+";\n\t rawpeak= "+str(numpy.around(rawpeak*bin, 1))
+";\n\t shift= "+str(numpy.around(shift, 1))
)
return shift, xfactor, peakdict['snr']
