def scaleTemplate(templatearray, scalefactor=1.0, boxsize=None):
if(templatearray.shape[0] != templatearray.shape[1]):
apDisplay.printWarning("template shape is NOT square, this may cause errors")
if abs(scalefactor - 1.0) > 0.01:
apDisplay.printMsg("scaling template by a factor of "+str(scalefactor))
templatearray = apImage.scaleImage(templatearray, scalefactor)
#make sure the box size is divisible by 16
if boxsize is not None or (templatearray.shape[0] % 16 != 0):
edgeavg = apImage.meanEdgeValue(templatearray)
origsize = templatearray.shape[0]
if boxsize is None:
# minimal padisize is 16
padsize = max(int(math.floor(float(origsize)/16)*16),16)
else:
padsize = boxsize
padshape = numpy.array([padsize,padsize])
apDisplay.printMsg("changing box size from "+str(origsize)+" to "+str(padsize))
if origsize > padsize:
#shrink image
templatearray = apImage.frame_cut(templatearray, padshape)
else:
#grow image
templatearray = apImage.frame_constant(templatearray, padshape, cval=edgeavg)
if templatearray.shape[0] < 20 or templatearray.shape[1] < 20:
apDisplay.printWarning("template is only "+str(templatearray.shape[0])+" pixels wide\n"+\
" and may only correlation noise in the image")
return templatearray
def scaleTemplate(templatearray, scalefactor=1.0, boxsize=None):
if (templatearray.shape[0] != templatearray.shape[1]):
apDisplay.printWarning(
"template shape is NOT square, this may cause errors")
if abs(scalefactor - 1.0) > 0.01:
apDisplay.printMsg("scaling template by a factor of " +
str(scalefactor))
templatearray = apImage.scaleImage(templatearray, scalefactor)
#make sure the box size is divisible by 16
if boxsize is not None or (templatearray.shape[0] % 16 != 0):
edgeavg = apImage.meanEdgeValue(templatearray)
origsize = templatearray.shape[0]
if boxsize is None:
# minimal padisize is 16
padsize = max(int(math.floor(float(origsize) / 16) * 16), 16)
else:
padsize = boxsize
padshape = numpy.array([padsize, padsize])
apDisplay.printMsg("changing box size from " + str(origsize) + " to " +
str(padsize))
if origsize > padsize:
#shrink image
templatearray = apImage.frame_cut(templatearray, padshape)
else:
#grow image
templatearray = apImage.frame_constant(templatearray,
padshape,
cval=edgeavg)
if templatearray.shape[0] < 20 or templatearray.shape[1] < 20:
apDisplay.printWarning("template is only "+str(templatearray.shape[0])+" pixels wide\n"+\
" and may only correlation noise in the image")
return templatearray
def real_fft3d(volume, *args, **kwargs):
padshape = numpy.asarray(volume.shape) * 1
padvolume = apImage.frame_constant(volume, padshape, volume.mean())
fft = fftpack.fftn(padvolume, *args, **kwargs)
return fft
def real_fft2d(image, *args, **kwargs):
padshape = numpy.asarray(image.shape) * 1
padimage = apImage.frame_constant(image, padshape, image.mean())
fft = fftpack.fft2(padimage, *args, **kwargs)
# normfft = (fft - fft.mean())/fft.std()
return fft
def processImage(self, imgdata):
self.ctfvalues = {}
bestdef = ctfdb.getBestCtfByResolution(imgdata, msg=True)
apix = apDatabase.getPixelSize(imgdata)
if (not (self.params['onepass'] and self.params['zeropass'])):
maskhighpass = False
ace2inputpath = os.path.join(imgdata['session']['image path'],imgdata['filename']+".mrc")
else:
maskhighpass = True
filterimg = apImage.maskHighPassFilter(imgdata['image'],apix,1,self.params['zeropass'],self.params['onepass'])
ace2inputpath = os.path.join(self.params['rundir'],imgdata['filename']+".mrc")
mrc.write(filterimg,ace2inputpath)
# make sure that the image is a square
dimx = imgdata['camera']['dimension']['x']
dimy = imgdata['camera']['dimension']['y']
if dimx != dimy:
dims = [dimx,dimy]
dims.sort()
apDisplay.printMsg("resizing image: %ix%i to %ix%i" % (dimx,dimy,dims[0],dims[0]))
mrcarray = apImage.mrcToArray(ace2inputpath,msg=False)
clippedmrc = apImage.frame_cut(mrcarray,[dims[0],dims[0]])
ace2inputpath = os.path.join(self.params['rundir'],imgdata['filename']+".mrc")
apImage.arrayToMrc(clippedmrc,ace2inputpath,msg=False)
### pad out image to speed up FFT calculations for non-standard image sizes
print "checking prime factor"
if primefactor.isGoodStack(dimx) is False:
goodsize = primefactor.getNextEvenPrime(dimx)
factor = float(goodsize) / float(dimx)
apDisplay.printMsg("padding image: %ix%i to %ix%i" % (dimx,dimy,dimx*factor,dimy*factor))
mrcarray = apImage.mrcToArray(ace2inputpath,msg=False)
# paddedmrc = imagefun.pad(mrcarray, None, factor)
paddedmrc = apImage.frame_constant(mrcarray, (dimx*factor,dimy*factor), cval=mrcarray.mean())
ace2inputpath = os.path.join(self.params['rundir'],imgdata['filename']+".mrc")
apImage.arrayToMrc(paddedmrc,ace2inputpath,msg=False)
inputparams = {
'input': ace2inputpath,
'cs': self.params['cs'],
'kv': imgdata['scope']['high tension']/1000.0,
'apix': apix,
'binby': self.params['bin'],
}
### make standard input for ACE 2
apDisplay.printMsg("Ace2 executable: "+self.ace2exe)
commandline = ( self.ace2exe
+ " -i " + str(inputparams['input'])
+ " -b " + str(inputparams['binby'])
+ " -c " + str(inputparams['cs'])
+ " -k " + str(inputparams['kv'])
+ " -a " + str(inputparams['apix'])
+ " -e " + str(self.params['edge_b'])+","+str(self.params['edge_t'])
+ " -r " + str(self.params['rotblur'])
+ "\n" )
### run ace2
apDisplay.printMsg("running ace2 at "+time.asctime())
apDisplay.printColor(commandline, "purple")
t0 = time.time()
if self.params['verbose'] is True:
ace2proc = subprocess.Popen(commandline, shell=True)
else:
aceoutf = open("ace2.out", "a")
aceerrf = open("ace2.err", "a")
ace2proc = subprocess.Popen(commandline, shell=True, stderr=aceerrf, stdout=aceoutf)
ace2proc.wait()
### check if ace2 worked
basename = os.path.basename(ace2inputpath)
imagelog = basename+".ctf.txt"
if not os.path.isfile(imagelog) and self.stats['count'] <= 1:
### ace2 always crashes on first image??? .fft_wisdom file??
time.sleep(1)
if self.params['verbose'] is True:
ace2proc = subprocess.Popen(commandline, shell=True)
else:
aceoutf = open("ace2.out", "a")
aceerrf = open("ace2.err", "a")
ace2proc = subprocess.Popen(commandline, shell=True, stderr=aceerrf, stdout=aceoutf)
ace2proc.wait()
if self.params['verbose'] is False:
aceoutf.close()
aceerrf.close()
if not os.path.isfile(imagelog):
lddcmd = "ldd "+self.ace2exe
lddproc = subprocess.Popen(lddcmd, shell=True)
lddproc.wait()
apDisplay.printError("ace2 did not run")
apDisplay.printMsg("ace2 completed in " + apDisplay.timeString(time.time()-t0))
### parse log file
self.ctfvalues = {
'cs': self.params['cs'],
'volts': imgdata['scope']['high tension'],
}
logf = open(imagelog, "r")
apDisplay.printMsg("reading log file %s"%(imagelog))
for line in logf:
sline = line.strip()
if re.search("^Final Defocus: ", sline):
### old ACE2
apDisplay.printError("This old version of ACE2 has a bug in the astigmastism, please upgrade ACE2 now")
#parts = sline.split()
#self.ctfvalues['defocus1'] = float(parts[2])
#self.ctfvalues['defocus2'] = float(parts[3])
### convert to degrees
#self.ctfvalues['angle_astigmatism'] = math.degrees(float(parts[4]))
elif re.search("^Final Defocus \(m,m,deg\):", sline):
### new ACE2
apDisplay.printMsg("Reading new ACE2 defocus")
parts = sline.split()
#print parts
self.ctfvalues['defocus1'] = float(parts[3])
self.ctfvalues['defocus2'] = float(parts[4])
# ace2 defines negative angle from +x toward +y
self.ctfvalues['angle_astigmatism'] = -float(parts[5])
elif re.search("^Amplitude Contrast:",sline):
parts = sline.split()
self.ctfvalues['amplitude_contrast'] = float(parts[2])
elif re.search("^Confidence:",sline):
parts = sline.split()
self.ctfvalues['confidence'] = float(parts[1])
self.ctfvalues['confidence_d'] = float(parts[1])
logf.close()
### summary stats
apDisplay.printMsg("============")
avgdf = (self.ctfvalues['defocus1']+self.ctfvalues['defocus2'])/2.0
ampconst = 100.0*self.ctfvalues['amplitude_contrast']
pererror = 100.0 * (self.ctfvalues['defocus1']-self.ctfvalues['defocus2']) / avgdf
apDisplay.printMsg("Defocus: %.3f x %.3f um (%.2f percent astigmatism)"%
(self.ctfvalues['defocus1']*1.0e6, self.ctfvalues['defocus2']*1.0e6, pererror ))
apDisplay.printMsg("Angle astigmatism: %.2f degrees"%(self.ctfvalues['angle_astigmatism']))
apDisplay.printMsg("Amplitude contrast: %.2f percent"%(ampconst))
apDisplay.printColor("Final confidence: %.3f"%(self.ctfvalues['confidence']),'cyan')
### double check that the values are reasonable
if avgdf > self.params['maxdefocus'] or avgdf < self.params['mindefocus']:
apDisplay.printWarning("bad defocus estimate, not committing values to database")
self.badprocess = True
if ampconst < 0.0 or ampconst > 80.0:
apDisplay.printWarning("bad amplitude contrast, not committing values to database")
self.badprocess = True
if self.ctfvalues['confidence'] < 0.2:
apDisplay.printWarning("bad confidence value, not committing values to database")
self.badprocess = True
## create power spectra jpeg
mrcfile = imgdata['filename']+".mrc.edge.mrc"
if os.path.isfile(mrcfile):
jpegfile = os.path.join(self.powerspecdir, apDisplay.short(imgdata['filename'])+".jpg")
ps = apImage.mrcToArray(mrcfile,msg=False)
c = numpy.array(ps.shape)/2.0
ps[c[0]-0,c[1]-0] = ps.mean()
ps[c[0]-1,c[1]-0] = ps.mean()
ps[c[0]-0,c[1]-1] = ps.mean()
ps[c[0]-1,c[1]-1] = ps.mean()
#print "%.3f -- %.3f -- %.3f"%(ps.min(), ps.mean(), ps.max())
ps = numpy.log(ps+1.0)
ps = (ps-ps.mean())/ps.std()
cutoff = -2.0*ps.min()
ps = numpy.where(ps > cutoff, cutoff, ps)
cutoff = ps.mean()
ps = numpy.where(ps < cutoff, cutoff, ps)
#print "%.3f -- %.3f -- %.3f"%(ps.min(), ps.mean(), ps.max())
apImage.arrayToJpeg(ps, jpegfile, msg=False)
apFile.removeFile(mrcfile)
self.ctfvalues['graph3'] = jpegfile
otherfiles = glob.glob(imgdata['filename']+".*.txt")
### remove extra debugging files
for filename in otherfiles:
if filename[-9:] == ".norm.txt":
continue
elif filename[-8:] == ".ctf.txt":
continue
else:
apFile.removeFile(filename)
if maskhighpass and os.path.isfile(ace2inputpath):
apFile.removeFile(ace2inputpath)
return
def real_fft3d(volume, *args, **kwargs):
padshape = numpy.asarray(volume.shape)*1
padvolume = apImage.frame_constant(volume, padshape, volume.mean())
fft = fftpack.fftn(padvolume, *args, **kwargs)
return fft
def real_fft2d(image, *args, **kwargs):
padshape = numpy.asarray(image.shape)*1
padimage = apImage.frame_constant(image, padshape, image.mean())
fft = fftpack.fft2(padimage, *args, **kwargs)
#normfft = (fft - fft.mean())/fft.std()
return fft
def real_fft2d(self, image, *args, **kwargs): padshape = numpy.asarray(image.shape)*1 padimage = apImage.frame_constant(image, padshape, image.mean()) fft = fftpack.fft2(padimage, *args, **kwargs) return fft
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 processImage(self, imgdata):
self.ctfvalues = {}
bestdef = ctfdb.getBestCtfByResolution(imgdata, msg=True)
apix = apDatabase.getPixelSize(imgdata)
if (not (self.params['onepass'] and self.params['zeropass'])):
maskhighpass = False
ace2inputpath = os.path.join(imgdata['session']['image path'],
imgdata['filename'] + ".mrc")
else:
maskhighpass = True
filterimg = apImage.maskHighPassFilter(imgdata['image'], apix, 1,
self.params['zeropass'],
self.params['onepass'])
ace2inputpath = os.path.join(self.params['rundir'],
imgdata['filename'] + ".mrc")
mrc.write(filterimg, ace2inputpath)
# make sure that the image is a square
dimx = imgdata['camera']['dimension']['x']
dimy = imgdata['camera']['dimension']['y']
if dimx != dimy:
dims = [dimx, dimy]
dims.sort()
apDisplay.printMsg("resizing image: %ix%i to %ix%i" %
(dimx, dimy, dims[0], dims[0]))
mrcarray = apImage.mrcToArray(ace2inputpath, msg=False)
clippedmrc = apImage.frame_cut(mrcarray, [dims[0], dims[0]])
ace2inputpath = os.path.join(self.params['rundir'],
imgdata['filename'] + ".mrc")
apImage.arrayToMrc(clippedmrc, ace2inputpath, msg=False)
### pad out image to speed up FFT calculations for non-standard image sizes
print "checking prime factor"
if primefactor.isGoodStack(dimx) is False:
goodsize = primefactor.getNextEvenPrime(dimx)
factor = float(goodsize) / float(dimx)
apDisplay.printMsg("padding image: %ix%i to %ix%i" %
(dimx, dimy, dimx * factor, dimy * factor))
mrcarray = apImage.mrcToArray(ace2inputpath, msg=False)
# paddedmrc = imagefun.pad(mrcarray, None, factor)
paddedmrc = apImage.frame_constant(mrcarray,
(dimx * factor, dimy * factor),
cval=mrcarray.mean())
ace2inputpath = os.path.join(self.params['rundir'],
imgdata['filename'] + ".mrc")
apImage.arrayToMrc(paddedmrc, ace2inputpath, msg=False)
inputparams = {
'input': ace2inputpath,
'cs': self.params['cs'],
'kv': imgdata['scope']['high tension'] / 1000.0,
'apix': apix,
'binby': self.params['bin'],
}
### make standard input for ACE 2
apDisplay.printMsg("Ace2 executable: " + self.ace2exe)
commandline = (self.ace2exe + " -i " + str(inputparams['input']) +
" -b " + str(inputparams['binby']) + " -c " +
str(inputparams['cs']) + " -k " +
str(inputparams['kv']) + " -a " +
str(inputparams['apix']) + " -e " +
str(self.params['edge_b']) + "," +
str(self.params['edge_t']) + " -r " +
str(self.params['rotblur']) + "\n")
### run ace2
apDisplay.printMsg("running ace2 at " + time.asctime())
apDisplay.printColor(commandline, "purple")
t0 = time.time()
if self.params['verbose'] is True:
ace2proc = subprocess.Popen(commandline, shell=True)
else:
aceoutf = open("ace2.out", "a")
aceerrf = open("ace2.err", "a")
ace2proc = subprocess.Popen(commandline,
shell=True,
stderr=aceerrf,
stdout=aceoutf)
ace2proc.wait()
### check if ace2 worked
basename = os.path.basename(ace2inputpath)
imagelog = basename + ".ctf.txt"
if not os.path.isfile(imagelog) and self.stats['count'] <= 1:
### ace2 always crashes on first image??? .fft_wisdom file??
time.sleep(1)
if self.params['verbose'] is True:
ace2proc = subprocess.Popen(commandline, shell=True)
else:
aceoutf = open("ace2.out", "a")
aceerrf = open("ace2.err", "a")
ace2proc = subprocess.Popen(commandline,
shell=True,
stderr=aceerrf,
stdout=aceoutf)
ace2proc.wait()
if self.params['verbose'] is False:
aceoutf.close()
aceerrf.close()
if not os.path.isfile(imagelog):
lddcmd = "ldd " + self.ace2exe
lddproc = subprocess.Popen(lddcmd, shell=True)
lddproc.wait()
apDisplay.printError("ace2 did not run")
apDisplay.printMsg("ace2 completed in " +
apDisplay.timeString(time.time() - t0))
### parse log file
self.ctfvalues = {
'cs': self.params['cs'],
'volts': imgdata['scope']['high tension'],
}
logf = open(imagelog, "r")
apDisplay.printMsg("reading log file %s" % (imagelog))
for line in logf:
sline = line.strip()
if re.search("^Final Defocus: ", sline):
### old ACE2
apDisplay.printError(
"This old version of ACE2 has a bug in the astigmastism, please upgrade ACE2 now"
)
#parts = sline.split()
#self.ctfvalues['defocus1'] = float(parts[2])
#self.ctfvalues['defocus2'] = float(parts[3])
### convert to degrees
#self.ctfvalues['angle_astigmatism'] = math.degrees(float(parts[4]))
elif re.search("^Final Defocus \(m,m,deg\):", sline):
### new ACE2
apDisplay.printMsg("Reading new ACE2 defocus")
parts = sline.split()
#print parts
self.ctfvalues['defocus1'] = float(parts[3])
self.ctfvalues['defocus2'] = float(parts[4])
# ace2 defines negative angle from +x toward +y
self.ctfvalues['angle_astigmatism'] = -float(parts[5])
elif re.search("^Amplitude Contrast:", sline):
parts = sline.split()
self.ctfvalues['amplitude_contrast'] = float(parts[2])
elif re.search("^Confidence:", sline):
parts = sline.split()
self.ctfvalues['confidence'] = float(parts[1])
self.ctfvalues['confidence_d'] = float(parts[1])
logf.close()
### summary stats
apDisplay.printMsg("============")
avgdf = (self.ctfvalues['defocus1'] + self.ctfvalues['defocus2']) / 2.0
ampconst = 100.0 * self.ctfvalues['amplitude_contrast']
pererror = 100.0 * (self.ctfvalues['defocus1'] -
self.ctfvalues['defocus2']) / avgdf
apDisplay.printMsg(
"Defocus: %.3f x %.3f um (%.2f percent astigmatism)" %
(self.ctfvalues['defocus1'] * 1.0e6,
self.ctfvalues['defocus2'] * 1.0e6, pererror))
apDisplay.printMsg("Angle astigmatism: %.2f degrees" %
(self.ctfvalues['angle_astigmatism']))
apDisplay.printMsg("Amplitude contrast: %.2f percent" % (ampconst))
apDisplay.printColor(
"Final confidence: %.3f" % (self.ctfvalues['confidence']), 'cyan')
### double check that the values are reasonable
if avgdf > self.params['maxdefocus'] or avgdf < self.params[
'mindefocus']:
apDisplay.printWarning(
"bad defocus estimate, not committing values to database")
self.badprocess = True
if ampconst < 0.0 or ampconst > 80.0:
apDisplay.printWarning(
"bad amplitude contrast, not committing values to database")
self.badprocess = True
if self.ctfvalues['confidence'] < 0.2:
apDisplay.printWarning(
"bad confidence value, not committing values to database")
self.badprocess = True
## create power spectra jpeg
mrcfile = imgdata['filename'] + ".mrc.edge.mrc"
if os.path.isfile(mrcfile):
jpegfile = os.path.join(
self.powerspecdir,
apDisplay.short(imgdata['filename']) + ".jpg")
ps = apImage.mrcToArray(mrcfile, msg=False)
c = numpy.array(ps.shape) / 2.0
ps[c[0] - 0, c[1] - 0] = ps.mean()
ps[c[0] - 1, c[1] - 0] = ps.mean()
ps[c[0] - 0, c[1] - 1] = ps.mean()
ps[c[0] - 1, c[1] - 1] = ps.mean()
#print "%.3f -- %.3f -- %.3f"%(ps.min(), ps.mean(), ps.max())
ps = numpy.log(ps + 1.0)
ps = (ps - ps.mean()) / ps.std()
cutoff = -2.0 * ps.min()
ps = numpy.where(ps > cutoff, cutoff, ps)
cutoff = ps.mean()
ps = numpy.where(ps < cutoff, cutoff, ps)
#print "%.3f -- %.3f -- %.3f"%(ps.min(), ps.mean(), ps.max())
apImage.arrayToJpeg(ps, jpegfile, msg=False)
apFile.removeFile(mrcfile)
self.ctfvalues['graph3'] = jpegfile
otherfiles = glob.glob(imgdata['filename'] + ".*.txt")
### remove extra debugging files
for filename in otherfiles:
if filename[-9:] == ".norm.txt":
continue
elif filename[-8:] == ".ctf.txt":
continue
else:
apFile.removeFile(filename)
if maskhighpass and os.path.isfile(ace2inputpath):
apFile.removeFile(ace2inputpath)
return
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 real_fft2d(self, image, *args, **kwargs): padshape = numpy.asarray(image.shape)*1 padimage = apImage.frame_constant(image, padshape, image.mean()) fft = fftpack.fft2(padimage, *args, **kwargs) return fft