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 runTestShift(img1name, img2name, imgpath, tiltdiff, coord): img1path = os.path.join(imgpath, img1name) img2path = os.path.join(imgpath, img2name) print img1path print img2path img1 = apImage.binImg(apImage.mrcToArray(img1path),4) img2 = apImage.binImg(apImage.mrcToArray(img2path),4) apImage.arrayToMrc(img1,"img1a-raw.mrc") apImage.arrayToMrc(img2,"img2a-raw.mrc") origin, newpart, snr = apTiltShift.getTiltedCoordinates(img1, img2, tiltdiff, coord) apImage.arrayToJpegPlusPeak(img1, "img1a-guess.jpg", (origin[1], origin[0])) apImage.arrayToJpegPlusPeak(img2, "img2a-guess.jpg", (newpart[1], newpart[0]))
def getImage(self, imgdata, binning):
imgarray = imgdata['image']
imgarray = apImage.binImg(imgarray, binning)
shape = numpy.shape(imgarray)
cutoff = 8.0
# remove spikes in the image first
imgarray = self.prepImage(imgarray, cutoff)
return imgarray
def getImage(self,imgdata,binning):
imgarray = imgdata['image']
imgarray = apImage.binImg(imgarray, binning)
shape=numpy.shape(imgarray)
cutoff=8.0
# remove spikes in the image first
imgarray=self.prepImage(imgarray,cutoff)
return imgarray
def makeInspectedMask(sessiondata,maskassessname,imgdata): assessruntree = getMaskAssessRunData(sessiondata,maskassessname) maskbins,maxbin = getMaskbins(sessiondata,maskassessname) allmaskarray = None for i,assessrundata in enumerate(assessruntree): maskrundata = assessrundata['maskrun'] maskregiondata = getMaskRegions(maskrundata,imgdata) if len(maskregiondata) == 0: continue keeplist = getRegionKeepList(assessrundata,maskregiondata) if len(keeplist) == 0: continue maskarray = getMaskArray(maskrundata,imgdata) # #### This is for auto msking only # if len(maskarray) == 0: # continue # else: # allmaskarray = maskarray # # return allmaskarray, maxbin # # ### End aoutomasking only code maskarray = apCrud.makeKeepMask(maskarray,keeplist) print maskarray extrabin = maxbin/maskbins[i] if extrabin > 1: maskarray = apImage.binImg(maskarray, extrabin) try: allmaskarray = allmaskarray+maskarray except: allmaskarray = maskarray allmaskarray = numpy.where(allmaskarray==0,0,1) if allmaskarray.shape == (): allmaskarray = None return allmaskarray,maxbin
def averageSubStack(partlist, stackfile, bin=1):
if len(partlist) > 300:
partlist = partlist[:300]
boxsize = apImagicFile.getBoxsize(stackfile)
if len(partlist) == 0:
binboxsize = boxsize/bin
blank = numpy.ones((binboxsize, binboxsize), dtype=numpy.float32)
return blank
if not os.path.isfile(stackfile):
apDisplay.printWarning("could not find stack, "+stackfile)
return False
partdatalist = apImagicFile.readParticleListFromStack(stackfile, partlist, boxsize, msg=False)
partdataarray = numpy.asarray(partdatalist)
finaldata = partdataarray.mean(0)
if bin > 1:
finaldata = apImage.binImg(finaldata, bin)
return finaldata
def createMontageInMemory(self, apix):
self.cluster_resolution = []
apDisplay.printMsg("Converting files")
### Set binning of images
boxsize = apImagicFile.getBoxsize(self.instack)
bin = 1
while boxsize/bin > 200:
bin+=1
binboxsize = boxsize/bin
### create averages
files = glob.glob(self.timestamp+".[0-9]*")
files.sort(self.sortFile)
montage = []
montagepngs = []
i = 0
for listname in files:
i += 1
apDisplay.printMsg("%d of %d classes"%(i,len(files)))
pngfile = listname+".png"
if not os.path.isfile(listname) or apFile.fileSize(listname) < 1:
### create a ghost particle
sys.stderr.write("skipping "+listname+"\n")
blank = numpy.ones((binboxsize, binboxsize), dtype=numpy.float32)
### add to montage stack
montage.append(blank)
self.cluster_resolution.append(None)
### create png
apImage.arrayToPng(blank, pngfile)
else:
### read particle list
partlist = self.readListFile(listname)
### average particles
partdatalist = apImagicFile.readParticleListFromStack(self.instack, partlist, boxsize, msg=False)
partdataarray = numpy.asarray(partdatalist)
finaldata = partdataarray.mean(0)
if bin > 1:
finaldata = apImage.binImg(finaldata, bin)
### add to montage stack
montage.append(finaldata)
res = apFourier.spectralSNR(partdatalist, apix)
self.cluster_resolution.append(res)
### create png
apImage.arrayToPng(finaldata, pngfile)
### check for png file
if os.path.isfile(pngfile):
montagepngs.append(pngfile)
else:
apDisplay.printError("failed to create montage")
stackname = "kerdenstack"+self.timestamp+".hed"
apImagicFile.writeImagic(montage, stackname)
### create montage
montagecmd = ("montage -geometry +4+4 -tile %dx%d "%(self.params['xdim'], self.params['ydim']))
for monpng in montagepngs:
montagecmd += monpng+" "
montagecmd += "montage.png"
apEMAN.executeEmanCmd(montagecmd, showcmd=True, verbose=False)
time.sleep(1)
apFile.removeFilePattern(self.timestamp+".*.png")
return bin
def processImage(self, imgdict):
from pyami import mrc
outimg = apImage.binImg(imgdict['image'], 2)
mrc.write(outimg, imgdict['filename']+"_sm.mrc")
def processImage(self, imgdict):
from pyami import mrc
outimg = apImage.binImg(imgdict['image'], 2)
mrc.write(outimg, imgdict['filename'] + "_sm.mrc")
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 getTiltedCoordinates(img1, img2, tiltdiff, picks1=[], angsearch=True, inittiltaxis=-7.2, 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)
picks1, list of particles picks for image 1
"""
t0 = time.time()
#shrink images
bin = 2
binned1 = apImage.binImg(img1, bin)
binned2 = apImage.binImg(img2, bin)
#apImage.arrayToJpeg(binned1, "binned1.jpg")
#apImage.arrayToJpeg(binned2, "binned2.jpg")
filt1 = apImage.highPassFilter(binned1, apix=1.0, radius=20.0, localbin=4/bin)
filt2 = apImage.highPassFilter(binned2, apix=1.0, radius=20.0, localbin=4/bin)
#apImage.arrayToJpeg(filt1, "filt1.jpg")
#apImage.arrayToJpeg(filt2, "filt2.jpg")
if angsearch is True:
bestsnr = 0
bestangle = None
### rough refine
#for angle in [-6, -4, -2,]:
# sys.stderr.write(".")
# shift, xfactor, snr = getTiltedRotateShift(filt1, filt2, tiltdiff, angle, bin, msg=False)
# if snr > bestsnr:
# bestsnr = snr
# bestangle = angle
bestangle = inittiltaxis
if msg is True:
apDisplay.printMsg("Best tilt axis angle= %.1f; SNR=%.2f"%(bestangle,bestsnr))
### finer refine
for angle in [bestangle-1, bestangle-0.5, bestangle+0.5, bestangle+1]:
if msg is True:
sys.stderr.write(".")
shift, xfactor, snr = getTiltedRotateShift(filt1, filt2, tiltdiff, angle, bin, msg=False)
if snr > bestsnr:
bestsnr = snr
bestangle = angle
if msg is True:
apDisplay.printMsg("Best tilt axis angle= %.1f; SNR=%.2f"%(bestangle,bestsnr))
### really fine refine
for angle in [bestangle-0.2, bestangle-0.1, bestangle+0.1, bestangle+0.2]:
if msg is True:
sys.stderr.write(".")
shift, xfactor, snr = getTiltedRotateShift(filt1, filt2, tiltdiff, angle, bin, msg=False)
if snr > bestsnr:
bestsnr = snr
bestangle = angle
if msg is True:
apDisplay.printMsg("Best tilt axis angle= %.1f; SNR=%.2f"%(bestangle,bestsnr))
shift, xfactor, snr = getTiltedRotateShift(filt1, filt2, tiltdiff, bestangle, bin, msg=msg)
if msg is True:
apDisplay.printMsg("Best tilt axis angle= %.1f; SNR=%.2f"%(bestangle,bestsnr))
else:
bestangle = 0.0
shift, xfactor, snr = getTiltedRotateShift(img1, img2, tiltdiff, bestangle, bin)
if msg and min(abs(shift)) < min(img1.shape)/16.0:
apDisplay.printWarning("Overlap was too close to the edge and possibly wrong.")
### case 1: find tilted center of first image
center = numpy.asarray(img1.shape)/2.0
newpoint = translatePoint(center, center, shift, bestangle, xfactor)
#print "newpoint=", newpoint
halfsh = (center + newpoint)/2.0
origin = halfsh
### case 2: using a list of picks
if len(picks1) > 1:
#get center most pick
dmin = origin[0]/2.0
for pick in picks1:
da = numpy.hypot(pick[0]-halfsh[0], pick[1]-halfsh[1])
if da < dmin:
dmin = da
origin = pick
# origin is pick from image 1
# newpart is pick from image 2
newpart = translatePoint(origin, center, shift, bestangle, xfactor)
newpart2 = numpy.array([(origin[0]*xfactor-shift[0])*xfactor, origin[1]-shift[1]])
if msg is True:
apDisplay.printMsg("origin=(%d,%d); newpart=(%.1f,%.1f); newpart2=(%.1f,%.1f)"
%(origin[0],origin[1], newpart[0],newpart[1], newpart2[0],newpart2[1],))
apDisplay.printMsg("completed in "+apDisplay.timeString(time.time()-t0))
return origin, newpart, snr, bestangle
### check to make sure points are not off the edge
while newpart[0] < 10:
newpart += numpy.asarray((20,0))
origin += numpy.asarray((20,0))
while newpart[1] < 10:
newpart += numpy.asarray((0,20))
origin += numpy.asarray((0,20))
while newpart[0] > img1.shape[0]-10:
newpart -= numpy.asarray((20,0))
origin -= numpy.asarray((20,0))
while newpart[1] > img1.shape[1]-10:
newpart -= numpy.asarray((0,20))
origin -= numpy.asarray((0,20))
return origin, newpart
def getTiltedCoordinates(img1, img2, tiltdiff, picks1=[], angsearch=True, inittiltaxis=-7.2, 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)
picks1, list of particles picks for image 1
"""
t0 = time.time()
#shrink images
bin = 2
binned1 = apImage.binImg(img1, bin)
binned2 = apImage.binImg(img2, bin)
#apImage.arrayToJpeg(binned1, "binned1.jpg")
#apImage.arrayToJpeg(binned2, "binned2.jpg")
filt1 = apImage.highPassFilter(binned1, apix=1.0, radius=20.0, localbin=4/bin)
filt2 = apImage.highPassFilter(binned2, apix=1.0, radius=20.0, localbin=4/bin)
#apImage.arrayToJpeg(filt1, "filt1.jpg")
#apImage.arrayToJpeg(filt2, "filt2.jpg")
if angsearch is True:
bestsnr = 0
bestangle = None
### rough refine
#for angle in [-6, -4, -2,]:
# sys.stderr.write(".")
# shift, xfactor, snr = getTiltedRotateShift(filt1, filt2, tiltdiff, angle, bin, msg=False)
# if snr > bestsnr:
# bestsnr = snr
# bestangle = angle
bestangle = inittiltaxis
if msg is True:
apDisplay.printMsg("Best tilt axis angle= %.1f; SNR=%.2f"%(bestangle,bestsnr))
### finer refine
for angle in [bestangle-1, bestangle-0.5, bestangle+0.5, bestangle+1]:
if msg is True:
sys.stderr.write(".")
shift, xfactor, snr = getTiltedRotateShift(filt1, filt2, tiltdiff, angle, bin, msg=False)
if snr > bestsnr:
bestsnr = snr
bestangle = angle
if msg is True:
apDisplay.printMsg("Best tilt axis angle= %.1f; SNR=%.2f"%(bestangle,bestsnr))
### really fine refine
for angle in [bestangle-0.2, bestangle-0.1, bestangle+0.1, bestangle+0.2]:
if msg is True:
sys.stderr.write(".")
shift, xfactor, snr = getTiltedRotateShift(filt1, filt2, tiltdiff, angle, bin, msg=False)
if snr > bestsnr:
bestsnr = snr
bestangle = angle
if msg is True:
apDisplay.printMsg("Best tilt axis angle= %.1f; SNR=%.2f"%(bestangle,bestsnr))
shift, xfactor, snr = getTiltedRotateShift(filt1, filt2, tiltdiff, bestangle, bin, msg=msg)
if msg is True:
apDisplay.printMsg("Best tilt axis angle= %.1f; SNR=%.2f"%(bestangle,bestsnr))
else:
bestangle = 0.0
shift, xfactor, snr = getTiltedRotateShift(img1, img2, tiltdiff, bestangle, bin)
if msg and min(abs(shift)) < min(img1.shape)/16.0:
apDisplay.printWarning("Overlap was too close to the edge and possibly wrong.")
### case 1: find tilted center of first image
center = numpy.asarray(img1.shape)/2.0
newpoint = translatePoint(center, center, shift, bestangle, xfactor)
#print "newpoint=", newpoint
halfsh = (center + newpoint)/2.0
origin = halfsh
### case 2: using a list of picks
if len(picks1) > 1:
#get center most pick
dmin = origin[0]/2.0
for pick in picks1:
da = numpy.hypot(pick[0]-halfsh[0], pick[1]-halfsh[1])
if da < dmin:
dmin = da
origin = pick
# origin is pick from image 1
# newpart is pick from image 2
newpart = translatePoint(origin, center, shift, bestangle, xfactor)
newpart2 = numpy.array([(origin[0]*xfactor-shift[0])*xfactor, origin[1]-shift[1]])
if msg is True:
apDisplay.printMsg("origin=(%d,%d); newpart=(%.1f,%.1f); newpart2=(%.1f,%.1f)"
%(origin[0],origin[1], newpart[0],newpart[1], newpart2[0],newpart2[1],))
apDisplay.printMsg("completed in "+apDisplay.timeString(time.time()-t0))
return origin, newpart, snr, bestangle
### check to make sure points are not off the edge
while newpart[0] < 10:
newpart += numpy.asarray((20,0))
origin += numpy.asarray((20,0))
while newpart[1] < 10:
newpart += numpy.asarray((0,20))
origin += numpy.asarray((0,20))
while newpart[0] > img1.shape[0]-10:
newpart -= numpy.asarray((20,0))
origin -= numpy.asarray((20,0))
while newpart[1] > img1.shape[1]-10:
newpart -= numpy.asarray((0,20))
origin -= numpy.asarray((0,20))
return origin, newpart
