def _cutoutForAlign2(fn, py, outFn=''): """ if not outFn, default with 'cut' extention resulting array is imgSequence=0 (t,w,z,y,x) return outFn """ h = imgManager.ImageManager(fn) slc, shiftZYX, ZYX = makeSlice(h, py) # input arr = N.empty((h.nt, h.nw, h.nz, h.ny, h.nx), h.dtype) for t in range(h.nt): for w in range(h.nw): arr[t, w] = h.get3DArr(w=w, t=t) canvas = N.squeeze(arr[slc].astype(arr.dtype.type)) newNum = (canvas.shape[-1], canvas.shape[-2], N.prod(canvas.shape[:-3])) if not outFn: outFn = '_'.join((h.filePath, EXT_CUTOUT)) #arr.Mrc.path, EXT_CUTOUT)) hdr = Mrc.makeHdrArray() Mrc.initHdrArrayFrom(hdr, h.hdr) #arr.Mrc.hdr) hdr.ImgSequence = 2 hdr.Num[:] = newNum mstart = [sl.start for sl in slc[::-1][:3] if isinstance(sl, slice)] hdr.mst[:len(mstart)] += mstart Mrc.save(canvas, outFn, ifExists='overwrite', hdr=hdr) return outFn
def __getitem__(self, idx): try: idx = int(idx) if idx < 0: idx = self.shape[0] + idx ws = range(self.nw) ts = range(self.nt) zs = range(self.nz) if self.maxaxs == 'w' and not self.ignor_color_axis: ws = [idx] self.sld_axs = 'w' elif self.maxaxs == 't' or (self.ignor_color_axis and self.ndim > 4): ts = [idx] self.sld_axs = 't' elif self.maxaxs == 'z' or (self.ignor_color_axis and self.ndim == 4): zs = [idx] self.sld_axs = 'z' else: raise ValueError, 'section axis not determined' if self.hdr.ImgSequence == 0: e = N.empty((len(ws), len(ts), len(zs), self.ny, self.nx), self.dtype) for wo, wi in enumerate(ws): for to, ti in enumerate(ts): for zo, zi in enumerate(zs): e[wo, to, zo] = self.img.getArr(w=wi, t=ti, z=zi) elif self.hdr.ImgSequence == 1: e = N.empty((len(ts), len(zs), len(ws), self.ny, self.nx), self.dtype) for to, ti in enumerate(ts): for zo, zi in enumerate(zs): for wo, wi in enumerate(ws): e[to, zo, wo] = self.img.getArr(w=wi, t=ti, z=zi) elif self.hdr.ImgSequence == 2: e = N.empty((len(ts), len(ws), len(zs), self.ny, self.nx), self.dtype) for to, ti in enumerate(ts): for wo, wi in enumerate(ws): for zo, zi in enumerate(zs): e[to, wo, zo] = self.img.getArr(w=wi, t=ti, z=zi) except TypeError: pass return N.squeeze(e)
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 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
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