def getShift(imgdata1, imgdata2):
# assumes images are square
print "Finding shift between", apDisplay.short(imgdata1["filename"]), "and", apDisplay.short(imgdata2["filename"])
dimension1 = imgdata1["camera"]["dimension"]["x"]
binning1 = imgdata1["camera"]["binning"]["x"]
dimension2 = imgdata2["camera"]["dimension"]["x"]
binning2 = imgdata2["camera"]["binning"]["x"]
finalsize = 512
# test to make sure images are at same mag
if imgdata1["scope"]["magnification"] != imgdata2["scope"]["magnification"]:
apDisplay.printWarning("Defocus pairs are at different magnifications, so shift can't be calculated.")
return None
# test to see if images capture the same area
if (dimension1 * binning1) != (dimension2 * binning2):
apDisplay.printWarning("Defocus pairs do not capture the same imaging area, so shift can't be calculated.")
return None
# images must not be less than finalsize (currently 512) pixels. This is arbitrary but for good reason
if dimension1 < finalsize or dimension2 < finalsize:
apDisplay.printWarning("Images must be greater than " + finalsize + " pixels to calculate shift.")
return None
# low pass filter 2 images to twice the final pixelsize BEFORE binning
shrinkfactor1 = dimension1 / finalsize
shrinkfactor2 = dimension2 / finalsize
binned1 = apImage.filterImg(imgdata1["image"], 1.0, shrinkfactor1 * 2)
binned2 = apImage.filterImg(imgdata2["image"], 1.0, shrinkfactor2 * 2)
# now bin 2 images
binned1 = apImage.binImg(binned1, shrinkfactor1)
binned2 = apImage.binImg(binned2, shrinkfactor2)
### fix for non-square images, correlation fails on non-square images
mindim = min(binned1.shape)
binned1 = binned1[:mindim, :mindim]
binned2 = binned2[:mindim, :mindim]
### use phase correlation, performs better than cross
pc = correlator.phase_correlate(binned1, binned2)
apImage.arrayToMrc(pc, "phaseCorrelate.mrc")
### find peak, filtering to 10.0 helps
peak = peakfinder.findSubpixelPeak(pc, lpf=10.0)
subpixpeak = peak["subpixel peak"]
shift = correlator.wrap_coord(subpixpeak, pc.shape)
peak["scalefactor"] = dimension2 / float(dimension1)
# print shift[0]*shrinkfactor1, shift[1]*shrinkfactor1
xshift = int(round(shift[0] * shrinkfactor1))
yshift = int(round(shift[1] * shrinkfactor1))
peak["shift"] = numpy.array((xshift, yshift))
apDisplay.printMsg("Determined shifts: %f %f" % (peak["shift"][0], peak["shift"][1]))
# print peak['shift']
# sys.exit(1)
return peak
def tmpRemoveCrud(params, imagefile):
bin = int(params["bin"])
apix = float(params["apix"])
diam = float(params["diam"])
lowpass = float(params["lp"])
pixrad = diam / apix / 2.0
#READ IMAGES
#imagefile=imagefile+'.mrc'
#image = Mrc.mrc_to_numeric(imagefile)
image = apDatabase.getImageData(imagefile)['image']
#BIN IMAGES
image = bin_img(image, bin)
#NORMALIZE
image = normStdev(image)
# image = PlaneRegression(image)
# image = normStdev(image)
#LOW PASS FILTER
image = apImage.filterImg(image, apix * float(bin), lowpass)
#BLACK OUT DARK AREAS, LESS THAN 2 STDEVS
image = removeCrud(image, imagefile, -1.0, params)
Mrc.numeric_to_mrc(image, (imagefile.split('.')[0] + '.dwn.mrc'))
return ()
def tmpRemoveCrud(params,imagefile):
bin = int(params["bin"])
apix = float(params["apix"])
diam = float(params["diam"])
lowpass = float(params["lp"])
pixrad = diam/apix/2.0
#READ IMAGES
#imagefile=imagefile+'.mrc'
#image = Mrc.mrc_to_numeric(imagefile)
image = apDatabase.getImageData(imagefile)['image']
#BIN IMAGES
image = bin_img(image,bin)
#NORMALIZE
image = normStdev(image)
# image = PlaneRegression(image)
# image = normStdev(image)
#LOW PASS FILTER
image = apImage.filterImg(image,apix*float(bin),lowpass)
#BLACK OUT DARK AREAS, LESS THAN 2 STDEVS
image = removeCrud(image,imagefile,-1.0,params)
Mrc.numeric_to_mrc(image,(imagefile.split('.')[0]+'.dwn.mrc'))
return()
def getShift(imgdata1, imgdata2):
#assumes images are square
print "Finding shift between", apDisplay.short(
imgdata1['filename']), "and", apDisplay.short(imgdata2['filename'])
dimension1 = imgdata1['camera']['dimension']['x']
binning1 = imgdata1['camera']['binning']['x']
dimension2 = imgdata2['camera']['dimension']['x']
binning2 = imgdata2['camera']['binning']['x']
finalsize = 512
#test to make sure images are at same mag
if imgdata1['scope']['magnification'] != imgdata2['scope']['magnification']:
apDisplay.printWarning(
"Defocus pairs are at different magnifications, so shift can't be calculated."
)
return None
#test to see if images capture the same area
if (dimension1 * binning1) != (dimension2 * binning2):
apDisplay.printWarning(
"Defocus pairs do not capture the same imaging area, so shift can't be calculated."
)
return None
#images must not be less than finalsize (currently 512) pixels. This is arbitrary but for good reason
if dimension1 < finalsize or dimension2 < finalsize:
apDisplay.printWarning("Images must be greater than " + finalsize +
" pixels to calculate shift.")
return None
#low pass filter 2 images to twice the final pixelsize BEFORE binning
shrinkfactor1 = dimension1 / finalsize
shrinkfactor2 = dimension2 / finalsize
binned1 = apImage.filterImg(imgdata1['image'], 1.0, shrinkfactor1 * 2)
binned2 = apImage.filterImg(imgdata2['image'], 1.0, shrinkfactor2 * 2)
#now bin 2 images
binned1 = apImage.binImg(binned1, shrinkfactor1)
binned2 = apImage.binImg(binned2, shrinkfactor2)
### fix for non-square images, correlation fails on non-square images
mindim = min(binned1.shape)
binned1 = binned1[:mindim, :mindim]
binned2 = binned2[:mindim, :mindim]
### use phase correlation, performs better than cross
pc = correlator.phase_correlate(binned1, binned2)
apImage.arrayToMrc(pc, "phaseCorrelate.mrc")
### find peak, filtering to 10.0 helps
peak = peakfinder.findSubpixelPeak(pc, lpf=10.0)
subpixpeak = peak['subpixel peak']
shift = correlator.wrap_coord(subpixpeak, pc.shape)
peak['scalefactor'] = dimension2 / float(dimension1)
#print shift[0]*shrinkfactor1, shift[1]*shrinkfactor1
xshift = int(round(shift[0] * shrinkfactor1))
yshift = int(round(shift[1] * shrinkfactor1))
peak['shift'] = numpy.array((xshift, yshift))
apDisplay.printMsg("Determined shifts: %f %f" %
(peak['shift'][0], peak['shift'][1]))
#print peak['shift']
#sys.exit(1)
return peak
