def mexhatFilter(a, mexSize=1):#, trimRatio=0.9): """ returned array is trimmed to remove edge """ global mexhatC, mexhatC_size, mexhatCf a = imgFilters.evenShapeArr(a) from Priithon.all import F as fftw try: if mexhatC.shape != a.shape: raise ValueError('go to except') if mexhatC_size != mexSize: raise ValueError('go to except') except NameError as ValueError: mexhatC_size = mexSize shape = N.asarray(a.shape, N.int)#N.float32) mexhatC = F.shift(F.mexhatArr(shape, scaleHalfMax=mexhatC_size, orig=None)) # orig 0.5 pixel does not work... mexhatCf = fftw.rfft(mexhatC) / N.multiply.reduce( shape ) ar = fftw.irfft( fftw.rfft(a.astype(N.float32)) * mexhatCf ) #if trimRatio < 1: # ar = trim3D(ar, trimRatio) #ar = imgFilters.maskEdgeWithValue2D(ar) # 2 pixels at the edges return ar
def phaseContrastFilter(a, inFourier=False, removeNan=True, nyquist=0.6): global GAUSS if inFourier: af = a.copy() else: af = F.rfft(a) # here is the phase contrast amp = N.abs(af) afa = af / amp if removeNan: afa = nanFilter(afa) # lowpass gaussian filter of phase image if nyquist: # since this takes long time, gaussian array is re-used if possible if GAUSS is not None and GAUSS[0] == nyquist and GAUSS[ 1].shape == afa.shape: nq, gf = GAUSS else: sigma = af.shape[-1] * nyquist gf = F.gaussianArr(afa.shape, sigma, peakVal=1, orig=0, wrap=(1, ) * (afa.ndim - 1) + (0, )) #, dtype=afa.dtype.type) GAUSS = (nyquist, gf) afa *= gf if inFourier: ap = afa else: ap = F.irfft(afa) return ap
def mexhatFilter(a, mexSize=1): #, trimRatio=0.9): """ returned array is trimmed to remove edge """ global mexhatC, mexhatC_size, mexhatCf a = imgFilters.evenShapeArr(a) from Priithon.all import F as fftw try: if mexhatC.shape != a.shape: raise ValueError('go to except') if mexhatC_size != mexSize: raise ValueError('go to except') except NameError as ValueError: mexhatC_size = mexSize shape = N.asarray(a.shape, N.int) #N.float32) mexhatC = F.shift( F.mexhatArr(shape, scaleHalfMax=mexhatC_size, orig=None)) # orig 0.5 pixel does not work... mexhatCf = fftw.rfft(mexhatC) / N.multiply.reduce(shape) ar = fftw.irfft(fftw.rfft(a.astype(N.float32)) * mexhatCf) #if trimRatio < 1: # ar = trim3D(ar, trimRatio) #ar = imgFilters.maskEdgeWithValue2D(ar) # 2 pixels at the edges return ar
def OnAutoFocus(self, evt=None): """ please read Chromagnon.alignfuncs.findBestRefZs() for detail of the logic """ if self.doc.nt > 1: t = int(self.tSliderBox.GetValue()) else: t = 0 ws = [ w for w, hist in enumerate(self.hist_toggleButton) if hist.GetValue() ] ms = N.zeros((len(ws), self.doc.nz), N.float32) # FFTW does not work with another program using it # here is the workaround for Chromagnon try: batch = self.GetParent().GetParent().GetParent() if hasattr(batch, 'th') and batch.th.isAlive(): for wi, w in enumerate(ws): arr = self.doc.get3DArr(t=t, w=w) for z in range(self.doc.nz): ms[wi, z] = N.prod(U.mmms(arr[z])[-2:]) # mean * std v, _, w, z = U.findMax(ms) self.zSliderBox.SetValue(str(z)) self.OnZSliderBox() self.OnAutoScale() G.openMsg( parent=self.parent, msg= 'A clumsy focusing method was used since another program was using FFTW.\nPlease wait for the better method until the other program is done.', title="Please wait") return except AttributeError: # no parent? pass # Frequency-based caluculation starts from Priithon.all import F ring = F.ringArr(self.doc.shape[-2:], radius1=self.doc.shape[-1] // 10, radius2=self.doc.shape[-2] // 4, orig=(0, 0), wrap=1) for wi, w in enumerate(ws): arr = self.doc.get3DArr(t=t, w=w) arr = arr / arr.mean() for z in range(self.doc.nz): af = F.rfft(N.ascontiguousarray(arr[z])) ar = af * ring[:, :af.shape[-1]] ms[wi, z] = N.sum(N.abs(ar)) v, _, w, z = U.findMax(ms) self.zSliderBox.SetValue(str(z)) self.OnZSliderBox() self.OnAutoScale()
def arr_edgeFilter(img, sigma=1.5): """ average-deviation with a gaussian prefilter img must be in an even shape """ if sigma: g = gaussianArrND(img.shape, sigma) g = F.shift(g) img = F.convolve(img.astype(N.float32), g) gr = N.gradient(img.astype(N.float32)) ff = N.sum(N.power(gr, 2), 0) return ff
def arr_edgeFilter(img, sigma=1.5): """ average-deviation with a gaussian prefilter img must be in an even shape """ if sigma: g = gaussianArrND(img.shape, sigma) g = F.shift(g) img = F.convolve(img.astype(N.float32), g) gr = N.gradient(img.astype(N.float32)) ff = N.sum(N.power(gr, 2), 0) return ff
def zoomFourier(arr, factor, use_abs=False): shape = N.array(arr.shape) target = [int(s) for s in shape * factor] #target[-1] //= 2 #target[-1] += 1 af = F.fft(arr) ap = paddingFourier(af, target) afp = F.ifft(ap) factor = target / shape if use_abs: return N.abs(afp) * N.product(factor) else: return N.real(afp) * N.product(factor)
def zoomFourier(arr, factor, use_abs=False): shape = N.array(arr.shape) target = [int(s) for s in shape * factor] #target[-1] //= 2 #target[-1] += 1 af = F.fft(arr) ap = paddingFourier(af, target) afp = F.ifft(ap) factor = target / shape if use_abs: return N.abs(afp) * N.product(factor) else: return N.real(afp) * N.product(factor)
def bandStopFilter(arr, x=4): """ to remove large pixelation artifact in Z """ #shape = N.asarray(arr.shape) #shape = N.where(shape % 2, shape+1, shape) #if N.sometrue(shape > arr.shape): # arr = imgFilters.paddingMed(arr, shape) fa = F.rfft2d(arr) fa[:x, 1:] = 0 fa[-x:, 1:] = 0 return F.irfft2d(fa)
def bandStopFilter(arr, x=4): """ to remove large pixelation artifact in Z """ #shape = N.asarray(arr.shape) #shape = N.where(shape % 2, shape+1, shape) #if N.sometrue(shape > arr.shape): # arr = imgFilters.paddingMed(arr, shape) fa = F.rfft2d(arr) fa[:x,1:] = 0 fa[-x:,1:] = 0 return F.irfft2d(fa)
def OnAutoFocus(self, evt=None): """ please read Chromagnon.alignfuncs.findBestRefZs() for detail of the logic """ if self.doc.nt > 1: t = int(self.tSliderBox.GetValue()) else: t = 0 ws = [w for w, hist in enumerate(self.hist_toggleButton) if hist.GetValue()] ms = N.zeros((len(ws),self.doc.nz), N.float32) # FFTW does not work with another program using it # here is the workaround for Chromagnon try: batch = self.GetParent().GetParent().GetParent() if hasattr(batch, 'th') and batch.th.isAlive(): for wi, w in enumerate(ws): arr = self.doc.get3DArr(t=t, w=w) for z in range(self.doc.nz): ms[wi,z] = N.prod(U.mmms(arr[z])[-2:]) # mean * std v,_,w,z = U.findMax(ms) self.zSliderBox.SetValue(str(z)) self.OnZSliderBox() self.OnAutoScale() G.openMsg(parent=self.parent, msg='A clumsy focusing method was used since another program was using FFTW.\nPlease wait for the better method until the other program is done.', title="Please wait") return except AttributeError: # no parent? pass # Frequency-based caluculation starts from Priithon.all import F ring = F.ringArr(self.doc.shape[-2:], radius1=self.doc.shape[-1]//10, radius2=self.doc.shape[-2]//4, orig=(0,0), wrap=1) for wi, w in enumerate(ws): arr = self.doc.get3DArr(t=t, w=w) arr = arr / arr.mean() for z in range(self.doc.nz): af = F.rfft(N.ascontiguousarray(arr[z])) ar = af * ring[:,:af.shape[-1]] ms[wi,z] = N.sum(N.abs(ar)) v,_,w,z = U.findMax(ms) self.zSliderBox.SetValue(str(z)) self.OnZSliderBox() self.OnAutoScale()
def shiftFullFFT(arr, delta=None): """ returns new array: arr shifted by delta (tuple) it uses fft (not rfft), multiplying with "shift array", ifft delta defaults to half of arr.shape """ shape = arr.shape if delta is None: delta = N.array(shape) / 2. elif not hasattr(delta, '__len__'): delta = (delta, ) * len(shape) elif len(shape) != len(delta): raise ValueError("shape and delta not same dimension") return F.ifft(F.fourierShiftArr(shape, delta) * F.fft(arr))
def shiftFullFFT(arr, delta=None): """ returns new array: arr shifted by delta (tuple) it uses fft (not rfft), multiplying with "shift array", ifft delta defaults to half of arr.shape """ shape = arr.shape if delta is None: delta = N.array(shape) / 2. elif not hasattr(delta, '__len__'): delta = (delta,)*len(shape) elif len(shape) != len(delta): raise ValueError("shape and delta not same dimension") return F.ifft(F.fourierShiftArr(shape, delta) * F.fft(arr))
def normalizedXcorr(a, b): std = N.std(a) * N.std(b) a_ = (a - N.mean(a)) / std b_ = (b - N.mean(b)) / std c = F.convolve(a_, b_, conj=1)# / a.size return c
def normalizedXcorr(a, b): std = N.std(a) * N.std(b) a_ = (a - N.mean(a)) / std b_ = (b - N.mean(b)) / std c = F.convolve(a_, b_, conj=1) # / a.size return c
def findBestRefZs(ref, sigma=0.5): """ PSF spread along the Z axis is often tilted in the Y or X axis. Thus simple maximum intensity projection may lead to the wrong answer to estimate rotation and magnification. On the other hands, taking a single section may also contain out of focus flare from neighboring sections that are tilted. Therefore, here sections with high complexity are selected and projected. An intensity-based method does not work for most (eg. tissue or bright field) images. Using variance is not enough for bright field image where the right focus loses contrast. Thus here we used sections containing higher frequency. ref: 3D array return z idx at the focus """ from Priithon.all import F nz = ref.shape[0] if ref.ndim == 2: return [0] elif nz <= 3: return range(nz) # ring array ring = F.ringArr(ref.shape[-2:], radius1=ref.shape[-1] // 10, radius2=ref.shape[-2] // 4, orig=(0, 0), wrap=1) # Due to the roll up after FFT, the edge sections in Z may contain different information among the channels. Thus these sections are left unused. ms = N.zeros((nz - 2, ), N.float32) for z in xrange(1, nz - 1): af = F.rfft(N.ascontiguousarray(ref[z])) ar = af * ring[:, :af.shape[-1]] ms[z - 1] = N.sum(N.abs(ar)) #ms[z-1] = N.prod(U.mmms(arr)[-2:]) # mean * std del ring, af, ar mi, ma, me, st = U.mmms(ms) thr = me + st * sigma ids = [idx for idx in range(1, nz - 1) if ms[idx - 1] > thr] if not ids: ids = range(1, nz - 1) return ids
def makeFiles(fns, std=10, div_step=50, div_max=800): #1000): """ makes a series of images added Gaussain and Poisson noise std: standard deviation for Gaussian, and mean=std*10 for Poisson div_step: the step that the original image is divided while noise is added div_max: the maximum value that the original image is divided return output filenames """ outs = [] for fn in fns: a = Mrc.bindFile(fn) for ns in NOISE_STATS: hdr = mrcIO.makeHdr_like(a.Mrc.hdr) if ns == NOISE_STATS[0]: noise = F.noiseArr(a.shape, stddev=std, mean=0) else: noise = F.poissonArr(a.shape, mean=std * 10) steps = range(div_step, div_max + div_step, div_step) ag = [a / c + noise for c in steps] for i, arr in enumerate(ag): val = steps[i] if hasattr(arr, "Mrc"): del arr.Mrc if arr.dtype == N.float64: arr = arr.astype(N.float32) hdr.PixelType = 2 out = a.Mrc.path + ns + '%04d' % val Mrc.save(arr, out, ifExists='overwrite', hdr=hdr) outs.append(out) return outs
def mexhatFilter(a, mexSize=1): #, trimRatio=0.9): """ returned array is trimmed to remove edge """ global mexhatC, mexhatC_size, mexhatCf a = imgFilters.evenShapeArr(a) try: # inside package from ..Priithon.all import F as fftw except ValueError: # Attempted relative import beyond toplevel package from Priithon.all import F as fftw #from Priithon.all import F as fftw try: if mexhatC.shape != a.shape: raise ValueError, 'go to except' if mexhatC_size != mexSize: raise ValueError, 'go to except' except NameError, ValueError: mexhatC_size = mexSize shape = N.asarray(a.shape, N.float32) mexhatC = F.shift( F.mexhatArr(shape, scaleHalfMax=mexhatC_size, orig=None)) # orig 0.5 pixel does not work... mexhatCf = fftw.rfft(mexhatC) / N.multiply.reduce(shape)
def phaseContrastFilter(a, inFourier=False, removeNan=True, nyquist=0.6): global GAUSS if inFourier: af = a.copy() else: af = F.rfft(a) # here is the phase contrast amp = N.abs(af) afa = af / amp if removeNan: afa = nanFilter(afa) # lowpass gaussian filter of phase image if nyquist: # since this takes long time, gaussian array is re-used if possible if GAUSS is not None and GAUSS[0] == nyquist and GAUSS[1].shape == afa.shape: nq, gf = GAUSS else: #sigma = af.shape[-1] * nyquist #gf = F.gaussianArr(afa.shape, sigma, peakVal=1, orig=0, wrap=(1,)*(afa.ndim-1)+(0,))#, dtype=afa.dtype.type) gshape = N.array(af.shape) if inFourier: gshape[-1] *= 2 sigma = gshape * nyquist gf = imgFilters.gaussianArrND(gshape, sigma, peakVal=1) gf = N.fft.fftshift(gf)[Ellipsis, :af.shape[-1]] GAUSS = (nyquist, gf) afa *= gf if inFourier: ap = afa else: ap = F.irfft(afa) return ap
def highPassF(af, highpassSigma=2.5, wiener=0.2, cutoffFreq=3): """ fourie space operations af: array after rfft half_nyx: half shape required for highpass filter highpassSigma: highpass filter, if 0, highpass is not done wiener: wiener coefficient for highpass filte cutoffFreq: band-pass around origin return: array BEFORE irfft WARNING: af will be changed, so use copy() if necessary """ global _G, _G_SHAPE if highpassSigma: shape = N.array(af.shape) shape[-1] = (shape[-1] - 1) * 2 szyx = shape / 2. if _G is not None and N.alltrue(_G_SHAPE == shape): g = _G else: g = imgFilters.gaussianArrND(shape, highpassSigma, peakVal=1, orig=szyx) g = F.shift(g)[..., :af.shape[-1]] _G = g _G_SHAPE = N.asarray(g.shape) g += wiener af /= g # kill DC af.flat[0] = 0 # kill lowest freq in YX for d in range(af.ndim - 2, af.ndim): upperdim = ':,' * d exec('af[%s0:cutoffFreq] = 0' % upperdim) return af
def highPassF(af, highpassSigma=2.5, wiener=0.2, cutoffFreq=3): """ fourie space operations af: array after rfft half_nyx: half shape required for highpass filter highpassSigma: highpass filter, if 0, highpass is not done wiener: wiener coefficient for highpass filte cutoffFreq: band-pass around origin return: array BEFORE irfft WARNING: af will be changed, so use copy() if necessary """ global _G, _G_SHAPE if highpassSigma: shape = N.array(af.shape) shape[-1] = (shape[-1] - 1) * 2 szyx = shape / 2. if _G is not None and N.alltrue(_G_SHAPE == shape): g = _G else: g = imgFilters.gaussianArrND(shape, highpassSigma, peakVal=1, orig=szyx) g = F.shift(g)[...,:af.shape[-1]] _G = g _G_SHAPE = N.asarray(g.shape) g += wiener af /= g # kill DC af.flat[0] = 0 # kill lowest freq in YX for d in range(af.ndim-2, af.ndim): upperdim = ':,' * d exec('af[%s0:cutoffFreq] = 0' % upperdim) return af
def paddingFourier(af, shape, value=0): afo = N.fft.fftshift(af) afp = F.getPadded(afo, shape, pad=value) return N.fft.ifftshift(afp)
def paddingFourier(af, shape, value=0): afo = N.fft.fftshift(af) afp = F.getPadded(afo, shape, pad=value) return N.fft.ifftshift(afp)
def Xcorr(a, b, phaseContrast=PHASE, nyquist=NYQUIST, removeEdge=0, gFit=True, win=11, ret=None, searchRad=None): """ sigma uses F.gaussianArr in the Fourier domain if ret is None: return zyx, xcf elif ret is 2: return s, v, zyx, xcf elif ret: return v, zyx, xcf """ #print 'phase contrast: %s' % str(phaseContrast) #global DATA # correct odd shape particularly Z axis a = N.squeeze(a) b = N.squeeze(b) a = imgFilters.evenShapeArr(a) b = imgFilters.evenShapeArr(b) shape = N.array(a.shape) # apodize a = apodize(a) b = apodize(b) # fourier transform af = F.rfft(a.astype(N.float32)) bf = F.rfft(b.astype(N.float32)) del a, b # phase contrast filter (removing any intensity information) if phaseContrast: afa = phaseContrastFilter(af, True, nyquist=nyquist) bfa = phaseContrastFilter(bf, True, nyquist=nyquist) else: afa = af bfa = bf del af, bf # removing edge if gaussian is not sufficient targetShape = shape - N.multiply(removeEdge, 2) if removeEdge: ap = imgFilters.cutOutCenter(F.irfft(afa), targetShape) bp = imgFilters.cutOutCenter(F.irfft(bfa), targetShape) afa = F.rfft(ap) bfa = F.rfft(bp) del ap, bp # shift array delta = targetShape / 2. shiftarr = F.fourierRealShiftArr(tuple(targetShape), delta) bfa *= shiftarr # cross correlation bfa = bfa.conjugate() c = cc = F.irfft(afa * bfa) center = N.divide(c.shape, 2) if searchRad: slc = imgGeo.nearbyRegion(c.shape, center, searchRad) cc = N.zeros_like(c) cc[slc] = c[slc] v, zyx, s = _findMaxXcor(cc, win, gFit=gFit) zyx -= center if ret == 2: return s, v, zyx, c elif ret: return v, zyx, c else: return zyx, c
def airyND(shape, orig=None, wrap=False): from Priithon.all import F from PriCommon import imgFit return F.radialArr(shape, imgFit.airy1D, orig, wrap)
def Xcorr(a, b, phaseContrast=PHASE, nyquist=NYQUIST, gFit=True, win=11, ret=None, searchRad=None, npad=4): """ sigma uses F.gaussianArr in the Fourier domain if ret is None: return zyx, xcf elif ret is 2: return s, v, zyx, xcf elif ret is 3: return zyx, xcf, a_phase_cotrast, b_phase_contrast elif ret: return v, zyx, xcf """ #print 'phase contrast: %s' % str(phaseContrast) #global DATA # correct odd shape particularly Z axis a = N.squeeze(a) b = N.squeeze(b) a = imgFilters.evenShapeArr(a) b = imgFilters.evenShapeArr(b) shape = N.array(a.shape) # padding strange shape #nyx = max(shape[-2:]) #pshape = N.array(a.shape[:-2] + (nyx,nyx)) # apodize a = paddAndApo(a, npad)#, pshape) #apodize(a) b = paddAndApo(b, npad)#, pshape) #apodize(b) # fourier transform af = F.rfft(a.astype(N.float32)) bf = F.rfft(b.astype(N.float32)) del a, b # phase contrast filter (removing any intensity information) if phaseContrast: afa = phaseContrastFilter(af, True, nyquist=nyquist) bfa = phaseContrastFilter(bf, True, nyquist=nyquist) else: afa = af bfa = bf del af, bf #targetShape = shape + (npad * 2) targetShape = shape + (npad * 2) # shift array delta = targetShape / 2. shiftarr = F.fourierRealShiftArr(tuple(targetShape), delta) bfa *= shiftarr # cross correlation bfa = bfa.conjugate() #c = cc = F.irfft(afa * bfa) c = F.irfft(afa * bfa) # 20180214 the padded region was cutout before finding the peak. c = cc = imgFilters.cutOutCenter(c, N.array(c.shape) - (npad * 2), interpolate=False) #cc = c center = N.divide(c.shape, 2) if searchRad: slc = imgGeo.nearbyRegion(c.shape, center, searchRad) cc = N.zeros_like(c) cc[slc] = c[slc] v, zyx, s = _findMaxXcor(cc, win, gFit=gFit) #return cc #print(zyx, center) zyx -= center #c = imgFilters.cutOutCenter(c, N.array(c.shape) - (npad * 2), interpolate=False) #c = imgFilters.cutOutCenter(c, shape, interpolate=False) if ret == 3: return zyx, c, F.irfft(afa), F.irfft(bfa) elif ret == 2: return s, v, zyx, c elif ret: return v, zyx, c else: return zyx, c
from Priithon.all import F as fftw #from Priithon.all import F as fftw try: if mexhatC.shape != a.shape: raise ValueError, 'go to except' if mexhatC_size != mexSize: raise ValueError, 'go to except' except NameError, ValueError: mexhatC_size = mexSize shape = N.asarray(a.shape, N.float32) mexhatC = F.shift( F.mexhatArr(shape, scaleHalfMax=mexhatC_size, orig=None)) # orig 0.5 pixel does not work... mexhatCf = fftw.rfft(mexhatC) / N.multiply.reduce(shape) ar = fftw.irfft(fftw.rfft(a.astype(N.float32)) * mexhatCf) #if trimRatio < 1: # ar = trim3D(ar, trimRatio) #ar = imgFilters.maskEdgeWithValue2D(ar) # 2 pixels at the edges return ar GAUSS = None def phaseContrastFilter(a, inFourier=False, removeNan=True, nyquist=0.6): global GAUSS if inFourier: af = a.copy() else:
def open_bh_sdt(fn, mode='r'): """ read Becker&Hickel SDT file into separate structures any error here might result from the file being corrupt """ #b = file(fn, 'rb').read() m = N.memmap(fn, mode=mode) hdrEnd = dtype_bhfile_header.itemsize lenDBhdr = dtype_BHFileBlockHeader.itemsize hdr = N.rec.array(m[:hdrEnd], dtype=dtype_bhfile_header) [0] #hdr = m[:hdrEnd].view(dtype_bhfile_header) if hdr['header_valid'] != BH_HEADER_VALID: print " * WARNING * SDT file file header not valid" infoStart = hdr['info_offs'] infoEnd = infoStart + hdr['info_length'] info= m[infoStart:infoEnd] info= info.view(N.dtype( (N.string_, len(info)) )) [0] setupStart= hdr['setup_offs'] setupEnd = setupStart + hdr['setup_length'] setup = m[setupStart:setupEnd] setup = setup.view(N.dtype( (N.string_, len(setup)) )) [0] setupTxtEnd = N.char.find(setup, '*END') + len('*END') setupTxt = setup[:setupTxtEnd] # following bytes are: '\r\n\r\n.......' # >>> U.hexdump(s.setup[s.setupTxtEnd:s.setupTxtEnd+21]) # 0000 0d 0a 0d 0a 42 49 4e 5f 50 41 52 41 5f 42 45 47 ....BIN_PARA_BEG # 0010 49 4e 3a 00 0e IN:.. #setupBin = nDBs = hdr['no_of_data_blocks'] if nDBs == 0x7fff: nDBs = hdr['reserved1'] #if nDBs != 1: # print " * WARNING * SDT file has more than one data block(nDBs=%s)"%(nDBs,) nMDBs = hdr['no_of_meas_desc_blocks'] mdbStart = hdr['meas_desc_block_offs'] lenMDB = hdr['meas_desc_block_length'] numMDB = hdr['no_of_meas_desc_blocks'] dataMeasInfos = m[mdbStart:mdbStart+numMDB*lenMDB].view(dtype=dtype_MeasureInfo) import weakref dbHdr = [] dbData = [] db0Start = hdr['data_block_offs'] o = db0Start for i in range(nDBs): dbh = m[o:o+lenDBhdr].view(dtype=dtype_BHFileBlockHeader) [0] dbHdr.append( dbh ) dbo = dbh['data_offs'] dbl = dbh['block_length'] #dbd = N.array(m[dbo:dbo+dbl], dtype=N.uint16) #ValueError: setting an array element with a sequence. dbd = m[dbo:dbo+dbl].view(dtype=N.uint16) #dbd.SDT_DataHdr = dbh #dbd.SDT_DataMeasInfo = weakref.proxy( measInfos[ dbh['meas_desc_block_no'] ] ) dbd = ndarray_inSdtFile(dbd, dataHdr=dbh, dataMeasInfo=dataMeasInfos[ dbh['meas_desc_block_no'] ] ) nx = dbd.SDT_DataMeasInfo['image_x'] ny = dbd.SDT_DataMeasInfo['image_y'] if nx>0 and ny >0: # FLIM !! dbd.shape = nx,ny,-1 dbd = dbd.T dbData.append(dbd) o = dbh['next_block_offs'] del dbh,dbd,dbo, dbl, nx, ny del o, i, weakref dataBlkHdrs = F.mockNDarray(*dbHdr) dataBlks = F.mockNDarray(*dbData) del dbHdr, dbData #return U.localsAsOneObject() #data = ndarray_inSdtFile(dbData[0], U.localsAsOneObject()) #data.SDT.data = weakref.proxy( self.) # remove circular reference dataBlks.SDT = U.localsAsOneObject() import weakref dataBlks.SDT.dataBlks = weakref.proxy( dataBlks ) # remove circular reference if len(dataBlks) == 1 and dataBlks[0].ndim>1: # one FLIM data block dataBlks[0].SDT = dataBlks.SDT.dataBlks return dataBlks[0] else: return dataBlks
def Xcorr(a, b, phaseContrast=PHASE, nyquist=NYQUIST, gFit=True, win=11, ret=None, searchRad=None, npad=4): """ sigma uses F.gaussianArr in the Fourier domain if ret is None: return zyx, xcf elif ret is 2: return s, v, zyx, xcf elif ret is 3: return zyx, xcf, a_phase_cotrast, b_phase_contrast elif ret: return v, zyx, xcf """ #print 'phase contrast: %s' % str(phaseContrast) #global DATA # correct odd shape particularly Z axis a = N.squeeze(a) b = N.squeeze(b) a = imgFilters.evenShapeArr(a) b = imgFilters.evenShapeArr(b) shape = N.array(a.shape) # padding strange shape #nyx = max(shape[-2:]) #pshape = N.array(a.shape[:-2] + (nyx,nyx)) # apodize a = paddAndApo(a, npad) #, pshape) #apodize(a) b = paddAndApo(b, npad) #, pshape) #apodize(b) # fourier transform af = F.rfft(a.astype(N.float32)) bf = F.rfft(b.astype(N.float32)) del a, b # phase contrast filter (removing any intensity information) if phaseContrast: afa = phaseContrastFilter(af, True, nyquist=nyquist) bfa = phaseContrastFilter(bf, True, nyquist=nyquist) else: afa = af bfa = bf del af, bf #targetShape = shape + (npad * 2) targetShape = shape + (npad * 2) # shift array delta = targetShape / 2. shiftarr = F.fourierRealShiftArr(tuple(targetShape), delta) bfa *= shiftarr # cross correlation bfa = bfa.conjugate() c = cc = F.irfft(afa * bfa) center = N.divide(c.shape, 2) if searchRad: slc = imgGeo.nearbyRegion(c.shape, center, searchRad) cc = N.zeros_like(c) cc[slc] = c[slc] v, zyx, s = _findMaxXcor(cc, win, gFit=gFit) zyx -= center c = imgFilters.cutOutCenter(c, N.array(c.shape) - (npad * 2), interpolate=False) #c = imgFilters.cutOutCenter(c, shape, interpolate=False) if ret == 3: return zyx, c, F.irfft(afa), F.irfft(bfa) elif ret == 2: return s, v, zyx, c elif ret: return v, zyx, c else: return zyx, c
def Xcorr(a, b, highpassSigma=2.5, wiener=0.2, cutoffFreq=3, forceSecondPeak=None, acceptOrigin=True, maskSigmaFact=1., removeY=None, removeX=None, ret=None, normalize=True, gFit=True, lap=None, win=11): """ returns (y,x), image if ret is True, returns [v, yx, image] to get yx cordinate of the image, yx += N.divide(picture.shape, 2) a, b: 2D array highpassSigma: sigma value used for highpass pre-filter wiener: wiener value used for highpass pre-filter cutoffFreq: kill lowest frequency component from 0 to this level forceSecondPeak: If input is n>0 (True is 1), pick up n-th peak acceptOrigin: If None, result at origin is rejected, look for the next peak maskSigmaFact: Modifier to remove previous peak to look for another peak removeYX: Rremove given number of pixel high intensity lines of the Xcorr Y: Vertical, X: Horizontal normalize: intensity normalized gFit: peak is fitted to 2D gaussian array, if None use center of mass win: window for gFit if b is a + (y,x) then, answer is (-y,-x) """ shapeA = N.asarray(a.shape) shapeB = N.asarray(b.shape) shapeM = N.max([shapeA, shapeB], axis=0) shapeM = N.where(shapeM % 2, shapeM+1, shapeM) center = shapeM / 2. arrs = [a,b] arrsS = ['a','b'] arrsF = [] for i, arr in enumerate(arrs): if arr.dtype not in [N.float32, N.float64]: arr = N.asarray(arr, N.float32) # this convolution has to be done beforehand to remove 2 pixels at the edge if lap == 'nothing': pass elif lap: arr = arr_Laplace(arr, mask=2) else: arr = arr_sorbel(arr, mask=1) if N.sometrue(shapeA < shapeM): arr = paddingMed(arr, shapeM) if normalize: mi, ma, me, sd = U.mmms(arr) arr = (arr - me) / sd if i ==1: arr = F.shift(arr) af = F.rfft(arr) af = highPassF(af, highpassSigma, wiener, cutoffFreq) arrsF.append(af) # start cross correlation af, bf = arrsF bf = bf.conjugate() cf = af * bf # go back to space domain c = F.irfft(cf) # c = _changeOrigin(cr) # removing lines if removeX: yi, xi = N.indices((removeX, shapeM[-1]))#sx)) yi += center[-2] - removeX/2.#sy/2 - removeX/2 c[yi, xi] = 0 if removeY: yi, xi = N.indices((shapeM[-2], removeY))#sy, removeY)) xi += center[-1] - removeY/2.#sx/2 - removeY/2 c[yi, xi] = 0 # find the first peak if gFit: v, yx, s = findMaxWithGFit(c, win=win)#, window=win, gFit=gFit) if v == 0: v, yx, s = findMaxWithGFit(c, win=win+2)#, window=win+2, gFit=gFit) if v == 0: v = U.findMax(c)[0] yx = N.add(yx, 0.5) #yx += 0.5 else: vzyx = U.findMax(c) v = vzyx[0] yx = vzyx[-2:] s = 2.5 yx -= center if N.alltrue(N.abs(yx) < 1.0) and not acceptOrigin: forceSecondPeak = True # forceSecondPeak: if not forceSecondPeak: forceSecondPeak = 0 for i in range(int(forceSecondPeak)): print('%i peak was removed' % (i+1)) #, sigma: %.2f' % (i+1, s) yx += center g = gaussianArr2D(c.shape, sigma=s/maskSigmaFact, peakVal=v, orig=yx) c = c - g #c = mask_gaussian(c, yx[0], yx[1], v, s) if gFit: v, yx, s = findMaxWithGFit(c, win=win)#, window=win, gFit=gFit) if v == 0: v, yx, s = findMaxWithGFit(c, win=win+2)#, window=win+2, gFit=gFit) if v == 0: v = U.findMax(c)[0] yx -= (center - 0.5) else: vzyx = U.findMax(c) v = vzyx[0] if not gFit: yx = centerOfMass(c, vzyx[-2:]) - center if lap is not 'nothing': c = paddingValue(c, shapeM+2) if ret == 2: return yx, af, bf.conjugate() elif ret: return v, yx, c else: return yx, c
def Xcorr(a, b, highpassSigma=2.5, wiener=0.2, cutoffFreq=3, forceSecondPeak=None, acceptOrigin=True, maskSigmaFact=1., removeY=None, removeX=None, ret=None, normalize=True, gFit=True, lap=None, win=11): """ returns (y,x), image if ret is True, returns [v, yx, image] to get yx cordinate of the image, yx += N.divide(picture.shape, 2) a, b: 2D array highpassSigma: sigma value used for highpass pre-filter wiener: wiener value used for highpass pre-filter cutoffFreq: kill lowest frequency component from 0 to this level forceSecondPeak: If input is n>0 (True is 1), pick up n-th peak acceptOrigin: If None, result at origin is rejected, look for the next peak maskSigmaFact: Modifier to remove previous peak to look for another peak removeYX: Rremove given number of pixel high intensity lines of the Xcorr Y: Vertical, X: Horizontal normalize: intensity normalized gFit: peak is fitted to 2D gaussian array, if None use center of mass win: window for gFit if b is a + (y,x) then, answer is (-y,-x) """ shapeA = N.asarray(a.shape) shapeB = N.asarray(b.shape) shapeM = N.max([shapeA, shapeB], axis=0) shapeM = N.where(shapeM % 2, shapeM + 1, shapeM) center = shapeM / 2. arrs = [a, b] arrsS = ['a', 'b'] arrsF = [] for i, arr in enumerate(arrs): if arr.dtype not in [N.float32, N.float64]: arr = N.asarray(arr, N.float32) # this convolution has to be done beforehand to remove 2 pixels at the edge if lap == 'nothing': pass elif lap: arr = arr_Laplace(arr, mask=2) else: arr = arr_sorbel(arr, mask=1) if N.sometrue(shapeA < shapeM): arr = paddingMed(arr, shapeM) if normalize: mi, ma, me, sd = U.mmms(arr) arr = (arr - me) / sd if i == 1: arr = F.shift(arr) af = F.rfft(arr) af = highPassF(af, highpassSigma, wiener, cutoffFreq) arrsF.append(af) # start cross correlation af, bf = arrsF bf = bf.conjugate() cf = af * bf # go back to space domain c = F.irfft(cf) # c = _changeOrigin(cr) # removing lines if removeX: yi, xi = N.indices((removeX, shapeM[-1])) #sx)) yi += center[-2] - removeX / 2. #sy/2 - removeX/2 c[yi, xi] = 0 if removeY: yi, xi = N.indices((shapeM[-2], removeY)) #sy, removeY)) xi += center[-1] - removeY / 2. #sx/2 - removeY/2 c[yi, xi] = 0 # find the first peak if gFit: v, yx, s = findMaxWithGFit(c, win=win) #, window=win, gFit=gFit) if v == 0: v, yx, s = findMaxWithGFit(c, win=win + 2) #, window=win+2, gFit=gFit) if v == 0: v = U.findMax(c)[0] yx = N.add(yx, 0.5) #yx += 0.5 else: vzyx = U.findMax(c) v = vzyx[0] yx = vzyx[-2:] s = 2.5 yx -= center if N.alltrue(N.abs(yx) < 1.0) and not acceptOrigin: forceSecondPeak = True # forceSecondPeak: if not forceSecondPeak: forceSecondPeak = 0 for i in range(int(forceSecondPeak)): print('%i peak was removed' % (i + 1)) #, sigma: %.2f' % (i+1, s) yx += center g = gaussianArr2D(c.shape, sigma=s / maskSigmaFact, peakVal=v, orig=yx) c = c - g #c = mask_gaussian(c, yx[0], yx[1], v, s) if gFit: v, yx, s = findMaxWithGFit(c, win=win) #, window=win, gFit=gFit) if v == 0: v, yx, s = findMaxWithGFit(c, win=win + 2) #, window=win+2, gFit=gFit) if v == 0: v = U.findMax(c)[0] yx -= (center - 0.5) else: vzyx = U.findMax(c) v = vzyx[0] if not gFit: yx = centerOfMass(c, vzyx[-2:]) - center if lap is not 'nothing': c = paddingValue(c, shapeM + 2) if ret == 2: return yx, af, bf.conjugate() elif ret: return v, yx, c else: return yx, c