def _smoothBorder(arr, start, stop, smooth, value): """ start, stop: [z,y,x] """ # prepare coordinates shape = N.array(arr.shape) start = N.ceil(start).astype(N.int16) stop = N.ceil(stop).astype(N.int16) smooth_start = start - smooth smooth_stop = stop + smooth smooth_start = N.where(smooth_start < 0, 0, smooth_start) smooth_stop = N.where(smooth_stop > shape, shape, smooth_stop) #print smooth_start, smooth_stop import copy sliceTemplate = [slice(None, None, None)] * arr.ndim shapeTemplate = list(shape) for d in range(arr.ndim): smooth_shape = shapeTemplate[:d] + shapeTemplate[d + 1:] # make an array containing the edge value edges = N.empty([2] + smooth_shape, N.float32) # start side slc = copy.copy(sliceTemplate) slc[d] = slice(start[d], start[d] + 1, None) edges[0] = arr[slc].reshape(smooth_shape) # stop side slc = copy.copy(sliceTemplate) slc[d] = slice(stop[d] - 1, stop[d], None) edges[1] = arr[slc].reshape(smooth_shape) edges = (edges - value) / float( smooth + 1) # this value can be array?? # both side for s, side in enumerate([start, stop]): if s == 0: rs = list(range(smooth_start[d], start[d])) rs.sort(reverse=True) elif s == 1: rs = list(range(stop[d], smooth_stop[d])) # smoothing for f, i in enumerate(rs): slc = copy.copy(sliceTemplate) slc[d] = slice(i, i + 1, None) edgeArr = edges[s].reshape(arr[slc].shape) #arr[slc] += edgeArr * (smooth - f) arr[slc] = arr[slc] + edgeArr * (smooth - f) # casting rule arr = N.ascontiguousarray(arr) return arr
def _smoothBorder(arr, start, stop, smooth, value): """ start, stop: [z,y,x] """ # prepare coordinates shape = N.array(arr.shape) start = N.ceil(start).astype(N.int16) stop = N.ceil(stop).astype(N.int16) smooth_start = start - smooth smooth_stop = stop + smooth smooth_start = N.where(smooth_start < 0, 0, smooth_start) smooth_stop = N.where(smooth_stop > shape, shape, smooth_stop) #print smooth_start, smooth_stop import copy sliceTemplate = [slice(None,None,None)] * arr.ndim shapeTemplate = list(shape) for d in range(arr.ndim): smooth_shape = shapeTemplate[:d] + shapeTemplate[d+1:] # make an array containing the edge value edges = N.empty([2] + smooth_shape, N.float32) # start side slc = copy.copy(sliceTemplate) slc[d] = slice(start[d], start[d]+1, None) edges[0] = arr[slc].reshape(smooth_shape) # stop side slc = copy.copy(sliceTemplate) slc[d] = slice(stop[d]-1, stop[d], None) edges[1] = arr[slc].reshape(smooth_shape) edges = (edges - value) / float(smooth + 1) # this value can be array?? # both side for s, side in enumerate([start, stop]): if s == 0: rs = list(range(smooth_start[d], start[d])) rs.sort(reverse=True) elif s == 1: rs = list(range(stop[d], smooth_stop[d])) # smoothing for f,i in enumerate(rs): slc = copy.copy(sliceTemplate) slc[d] = slice(i,i+1,None) edgeArr = edges[s].reshape(arr[slc].shape) #arr[slc] += edgeArr * (smooth - f) arr[slc] = arr[slc] + edgeArr * (smooth - f) # casting rule arr = N.ascontiguousarray(arr) return arr
def img2polar2D(img, center, final_radius=None, initial_radius=None, phase_width=360, return_idx=False): """ img: array center: coordinate y, x final_radius: ending radius initial_radius: starting radius phase_width: npixles / circle return_idx: return transformation coordinates (y,x) """ if img.ndim > 2 or len(center) > 2: raise ValueError( 'this function only support 2D, you entered %i-dim array and %i-dim center coordinate' % (img.ndim, len(center))) if initial_radius is None: initial_radius = 0 if final_radius is None: rad0 = N.ceil(N.array(img.shape) - center) final_radius = min((int(min(rad0)), int(min(N.ceil(center))))) if phase_width is None: phase_width = N.sum(img.shape[-2:]) * 2 theta, R = np.meshgrid(np.linspace(0, 2 * np.pi, phase_width), np.arange(initial_radius, final_radius)) Ycart, Xcart = polar2cart2D(R, theta, center) Ycart = N.where(Ycart >= img.shape[0], img.shape[0] - 1, Ycart) Xcart = N.where(Xcart >= img.shape[1], img.shape[1] - 1, Xcart) Ycart = Ycart.astype(int) Xcart = Xcart.astype(int) polar_img = img[Ycart, Xcart] polar_img = np.reshape(polar_img, (final_radius - initial_radius, phase_width)) if return_idx: return polar_img, Ycart, Xcart else: return polar_img
def apodize(img, napodize=10, doZ=True): """ softens the edges of a singe xy section to reduce edge artifacts and improve the fits return copy of the img """ img = img.copy() img = img.astype(N.float32) # casting rule # determine napodize shape = N.array(img.shape) // 2 napodize = N.where(shape < napodize, shape, napodize) if doZ and img.ndim >= 3 and img.shape[0] > 3: rr = list(range(-3,0)) else: rr = list(range(-2,0)) for idx in rr: fact = N.arange(1./napodize[idx],napodize[idx],1./napodize[idx], dtype=N.float32)[:napodize[idx]] for napo in range(napodize[idx]): slc0 = [Ellipsis,slice(napo, napo+1)] + [slice(None)] * abs(idx+1) if not napo: slc1 = [Ellipsis,slice(-(napo+1),None)] + [slice(None)] * abs(idx+1) else: slc1 = [Ellipsis,slice(-(napo+1),-(napo))] + [slice(None)] * abs(idx+1) img[slc0] *= fact[napo] img[slc1] *= fact[napo] #img[slc0] = img[slc0] * fact[napo] # casting rule #img[slc1] = img[scl1] * fact[napo] return img
def apodize(img, napodize=10, doZ=True): """ softens the edges of a singe xy section to reduce edge artifacts and improve the fits return copy of the img """ img = img.copy() img = img.astype(N.float32) # casting rule # determine napodize shape = N.array(img.shape) // 2 napodize = N.where(shape < napodize, shape, napodize) if doZ and img.ndim >= 3 and img.shape[0] > 3: rr = range(-3, 0) else: rr = range(-2, 0) for idx in rr: fact = N.arange(1. / napodize[idx], napodize[idx], 1. / napodize[idx], dtype=N.float32)[:napodize[idx]] for napo in range(napodize[idx]): slc0 = [Ellipsis, slice(napo, napo + 1) ] + [slice(None)] * abs(idx + 1) if not napo: slc1 = [Ellipsis, slice(-(napo + 1), None) ] + [slice(None)] * abs(idx + 1) else: slc1 = [Ellipsis, slice(-(napo + 1), -(napo)) ] + [slice(None)] * abs(idx + 1) img[slc0] *= fact[napo] img[slc1] *= fact[napo] #img[slc0] = img[slc0] * fact[napo] # casting rule #img[slc1] = img[scl1] * fact[napo] return img
def mask_gaussianND(arr, zyx, v, sigma=2., ret=None, rot=0, clipZero=True): ''' subtract elliptical gaussian at y,x with peakVal v if ret, return arr, else, arr itself is edited ''' from . import imgGeo zyx = N.asarray(zyx) ndim = arr.ndim shape = N.array(arr.shape) try: if len(sigma) != ndim: raise ValueError('len(sigma) must be the same as len(shape)') else: sigma = N.asarray(sigma) except TypeError:#(TypeError, ValueError): sigma = N.asarray([sigma]*ndim) # prepare small window slc = imgGeo.nearbyRegion(shape, N.floor(zyx), sigma * 10) inds, LD = imgFit.rotateIndicesND(slc, dtype=N.float32, rot=rot) param = (0, v,) + tuple(zyx) + tuple(sigma) sidx = 2 + ndim g = imgFit.yGaussianND(N.asarray(param), inds, sidx).astype(arr.dtype.type) roi = arr[slc] if clipZero: g = N.where(g > roi, roi, g) if ret: e = N.zeros_like(arr) e[slc] = g # this may be faster than copy() return arr - e else: arr[slc] -= g
def nanFilter(af, kernel=3): """ 3D phase contrast filter often creates 'nan' this filter removes nan by averaging surrounding pixels return af """ af = af.copy() shape = N.array(af.shape) radius = N.subtract(kernel, 1) // 2 box = kernel * af.ndim nan = N.isnan(af) nids = N.array(N.nonzero(nan)).T for nidx in nids: slc = [slice(idx,idx+1) for idx in nidx] slices = [] for dim in range(af.ndim): slc2 = slice(slc[dim].start - radius, slc[dim].stop + radius) while slc2.start < 0: slc2 = slice(slc2.start + 1, slc2.stop) while slc2.stop > shape[dim]: slc2 = slice(slc2.start, slc2.stop -1) slices.append(slc2) val = af[slices] nanlocal = N.isnan(val) ss = N.sum(N.where(nanlocal, 0, val)) / float((box - N.sum(nanlocal))) af[slc] = ss return af
def nanFilter(af, kernel=3): """ 3D phase contrast filter often creates 'nan' this filter removes nan by averaging surrounding pixels return af """ af = af.copy() shape = N.array(af.shape) radius = N.subtract(kernel, 1) // 2 box = kernel * af.ndim nan = N.isnan(af) nids = N.array(N.nonzero(nan)).T for nidx in nids: slc = [slice(idx, idx + 1) for idx in nidx] slices = [] for dim in range(af.ndim): slc2 = slice(slc[dim].start - radius, slc[dim].stop + radius) while slc2.start < 0: slc2 = slice(slc2.start + 1, slc2.stop) while slc2.stop > shape[dim]: slc2 = slice(slc2.start, slc2.stop - 1) slices.append(slc2) val = af[slices] nanlocal = N.isnan(val) ss = N.sum(N.where(nanlocal, 0, val)) / float((box - N.sum(nanlocal))) af[slc] = ss return af
def img2polar2D(img, center, final_radius=None, initial_radius = None, phase_width = 360, return_idx=False): """ img: array center: coordinate y, x final_radius: ending radius initial_radius: starting radius phase_width: npixles / circle return_idx: return transformation coordinates (y,x) """ if img.ndim > 2 or len(center) > 2: raise ValueError('this function only support 2D, you entered %i-dim array and %i-dim center coordinate' % (img.ndim, len(center))) if initial_radius is None: initial_radius = 0 if final_radius is None: rad0 = N.ceil(N.array(img.shape) - center) final_radius = min((int(min(rad0)), int(min(N.ceil(center))))) if phase_width is None: phase_width = N.sum(img.shape[-2:]) * 2 theta , R = np.meshgrid(np.linspace(0, 2*np.pi, phase_width), np.arange(initial_radius, final_radius)) Ycart, Xcart = polar2cart2D(R, theta, center) Ycart = N.where(Ycart >= img.shape[0], img.shape[0]-1, Ycart) Xcart = N.where(Xcart >= img.shape[1], img.shape[1]-1, Xcart) Ycart = Ycart.astype(int) Xcart = Xcart.astype(int) polar_img = img[Ycart,Xcart] polar_img = np.reshape(polar_img,(final_radius-initial_radius,phase_width)) if return_idx: return polar_img, Ycart, Xcart else: return polar_img
def keepShape(a, shape, difmod=None): canvas = N.zeros(shape, a.dtype.type) if difmod is None: dif = (shape - N.array(a.shape, N.float32)) / 2. mod = N.ceil(N.mod(dif, 1)) else: dif, mod = difmod dif = N.where(dif > 0, N.ceil(dif), N.floor(dif)) # smaller aoff = N.where(dif < 0, 0, dif) aslc = [slice(dp, shape[i]-dp+mod[i]) for i, dp in enumerate(aoff)] # larger coff = N.where(dif > 0, 0, -dif) cslc = [slice(dp, a.shape[i]-dp+mod[i]) for i, dp in enumerate(coff)] canvas[aslc] = a[cslc] if difmod is None: return canvas, mod else: return canvas
def keepShape(a, shape, difmod=None): canvas = N.zeros(shape, a.dtype.type) if difmod is None: dif = (shape - N.array(a.shape, N.float32)) / 2. mod = N.ceil(N.mod(dif, 1)) else: dif, mod = difmod dif = N.where(dif > 0, N.ceil(dif), N.floor(dif)) # smaller aoff = N.where(dif < 0, 0, dif) aslc = [slice(dp, shape[i] - dp + mod[i]) for i, dp in enumerate(aoff)] # larger coff = N.where(dif > 0, 0, -dif) cslc = [slice(dp, a.shape[i] - dp + mod[i]) for i, dp in enumerate(coff)] canvas[aslc] = a[cslc] if difmod is None: return canvas, mod else: return canvas
def mask_gaussianND(arr, zyx, v, sigma=2., ret=None, rot=0, clipZero=True): ''' subtract elliptical gaussian at y,x with peakVal v if ret, return arr, else, arr itself is edited ''' from . import imgGeo zyx = N.asarray(zyx) ndim = arr.ndim shape = N.array(arr.shape) try: if len(sigma) != ndim: raise ValueError('len(sigma) must be the same as len(shape)') else: sigma = N.asarray(sigma) except TypeError: #(TypeError, ValueError): sigma = N.asarray([sigma] * ndim) # prepare small window slc = imgGeo.nearbyRegion(shape, N.floor(zyx), sigma * 10) inds, LD = imgFit.rotateIndicesND(slc, dtype=N.float32, rot=rot) param = ( 0, v, ) + tuple(zyx) + tuple(sigma) sidx = 2 + ndim g = imgFit.yGaussianND(N.asarray(param), inds, sidx).astype(arr.dtype.type) roi = arr[slc] if clipZero: g = N.where(g > roi, roi, g) if ret: e = N.zeros_like(arr) e[slc] = g # this may be faster than copy() return arr - e else: arr[slc] -= g
def rotateIndicesND(slicelist, dtype=N.float64, rot=0, mode=2): """ slicelist: even shape works much better than odd shape rot: counter-clockwise, xy-plane mode: testing different ways of doing, (1 or 2 and the same result) return inds, LD """ global INDS_DIC shape = [] LD = [] for sl in slicelist: if isinstance(sl, slice): shape.append(sl.stop - sl.start) LD.append(sl.start) shapeTuple = tuple(shape + [rot]) if shapeTuple in INDS_DIC: inds = INDS_DIC[shapeTuple] else: shape = N.array(shape) ndim = len(shape) odd_even = shape % 2 s2 = N.ceil(shape * (2**0.5)) if mode == 1: # everything is even s2 = N.where(s2 % 2, s2 + 1, s2) elif mode == 2: # even & even or odd & odd for d, s in enumerate(shape): if (s % 2 and not s2[d] % 2) or (not s % 2 and s2[d] % 2): s2[d] += 1 cent = s2 / 2. dif = (s2 - shape) / 2. dm = dif % 1 # print s2, cent, dif, dm slc = [Ellipsis] + [ slice(int(d), int(d) + shape[i]) for i, d in enumerate(dif) ] # This slice is float which shift array when cutting out!! s2 = tuple([int(ss) for ss in s2]) # numpy array cannot use used for slice inds = N.indices(s2, N.float32) ind_shape = inds.shape nz = N.product(ind_shape[:-2]) nsec = nz / float(ndim) if ndim > 2: inds = N.reshape(inds, (nz, ) + ind_shape[-2:]) irs = N.empty_like(inds) for d, ind in enumerate(inds): idx = int(d // nsec) c = cent[idx] if rot and inds.ndim > 2: U.trans2d(ind - c, irs[d], (0, 0, rot, 1, 0, 1)) irs[d] += c - dif[idx] else: irs[d] = ind - dif[idx] if len(ind_shape) > 2: irs = N.reshape(irs, ind_shape) irs = irs[slc] if mode == 1 and N.sometrue(dm): inds = N.empty_like(irs) # print 'translate', dm for d, ind in enumerate(irs): U.trans2d(ind, inds[d], (-dm[1], -dm[0], 0, 1, 0, 1)) else: inds = irs INDS_DIC[shapeTuple] = inds r_inds = N.empty_like(inds) for d, ld in enumerate(LD): r_inds[d] = inds[d] + ld return r_inds, LD
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 pointsCutOut3D(arr, posList, windowSize=100, d2=None, interpolate=True, removeWrongShape=True): """ array: nd array posList: ([(z,)y,x]...) windowSize: scalar (in pixel or as percent < 1.) or ((z,)y,x) if arr.ndim > 2, and len(windowSize) == 2, then cut out section-wise (higher dimensions stay the same) d2: conern only XY of windowSize (higher dimensions stay the same) interpolate: shift array by subpixel interpolation to adjust center return: list of array centered at each pos in posList """ shape = N.array(arr.shape) center = shape / 2. if arr.ndim <= 2: ndim_arr = arr.ndim else: ndim_arr = 3 # prepare N-dimensional window size try: len(windowSize) # seq if d2: windowSize = windowSize[-2:] if len(windowSize) != arr.ndim: dim = len(windowSize) windowSize = tuple(shape[:-dim]) + tuple(windowSize) except TypeError: # scaler if windowSize < 1 and windowSize > 0: # percentage w = shape * windowSize if d2: w[:-2] = shape[:-2] elif ndim_arr > 3: w[:-3] = shape[:-3] windowSize = w.astype(N.uint) else: windowSize = N.where(shape >= windowSize, windowSize, shape) if d2: windowSize[:-2] = shape[:-2] windowSize = N.asarray(windowSize) halfWin = windowSize / 2 #ndim_win = len(windowSize) margin = int(interpolate) # cutout individual position arrList = [] for pos in posList: ndim_pos = len(pos) # calculate idx dif = pos +0.5 - halfWin[-ndim_pos:] dif = N.round_(dif) dif = dif.astype(N.int) starts = dif - margin starts = N.where(starts < 0, 0, starts) stops = dif + windowSize[-ndim_pos:] + margin stops = N.where(stops > shape[-ndim_pos:], shape[-ndim_pos:], stops) #if removeWrongShape and (N.any((starts) < 0) or N.any((stops) > shape[-ndim_pos:])): # continue slc = [Ellipsis] for dim, s0 in enumerate(starts): s1 = stops[dim] slc.append(slice(s0, s1)) cpa = arr[slc] if interpolate: dif = pos + 0.5 - halfWin[-ndim_pos:] sub = (arr.ndim - len(dif)) * [0] + list(dif) sub = N.array(sub) sar = U.nd.shift(cpa, sub % 1) slc = [Ellipsis] for d in range(ndim_arr): slc.append(slice(margin, -margin)) cpa = sar[slc] arrList.append(cpa) if removeWrongShape: if d2 and arr.ndim == 2: arrList = N.array([a for a in arrList if N.all(a.shape[-2:] == windowSize[-2:])]) else: arrList = N.array([a for a in arrList if N.all(a.shape[-3:] == windowSize[-3:])]) return arrList
def pointsCutOutND(arr, posList, windowSize=100, sectWise=None, interpolate=True): """ array: nd array posList: ([(z,)y,x]...) windowSize: scalar (in pixel or as percent < 1.) or ((z,)y,x) if arr.ndim > 2, and len(windowSize) == 2, then cut out section-wise (higher dimensions stay the same) sectWise: conern only XY of windowSize (higher dimensions stay the same) interpolate: shift array by subpixel interpolation to adjust center return: list of array centered at each pos in posList """ shape = N.array(arr.shape) center = shape / 2. # prepare N-dimensional window size try: len(windowSize) # seq if sectWise: windowSize = windowSize[-2:] if len(windowSize) != arr.ndim: dim = len(windowSize) windowSize = tuple(shape[:-dim]) + tuple(windowSize) except TypeError: # scaler if windowSize < 1 and windowSize > 0: # percentage w = shape * windowSize if sectWise: w[:-2] = shape[:-2] windowSize = w.astype(N.uint16) else: windowSize = N.where(shape >= windowSize, windowSize, shape) if sectWise: windowSize = arr.shape[:-2] + tuple(windowSize[-2:]) windowSize = N.asarray(windowSize) # cutout individual position arrList=[] for pos in posList: # prepare N-dimensional coordinate n = len(pos) if n != len(windowSize): temp = center.copy() center[-n:] = pos pos = center # calculate idx ori = pos - (windowSize / 2.) # float value oidx = N.ceil(ori) # idx subpxl = oidx - ori # subpixel mod if interpolate and N.sometrue(subpxl): # comit to make shift SHIFT = 1 else: SHIFT = 0 # prepare slice # when comitted to make shift, first cut out window+1, # then make subpixle shift, and then cutout 1 edge slc = [Ellipsis] # Ellipsis is unnecessary, just in case... slc_edge = [slice(1,-1,None)] * arr.ndim for d in range(arr.ndim): start = oidx[d] - SHIFT if start < 0: start = 0 slc_edge[d] = slice(0, slc_edge[d].stop, None) stop = oidx[d] + windowSize[d] + SHIFT if stop > shape[d]: stop = shape[d] slc_edge[d] = slice(slc_edge[d].start, shape[d], None) slc += [slice(int(start), int(stop), None)] # cutout, shift and cutout #print(slc, slc_edge) try: canvas = arr[slc] if SHIFT: # 20180214 subpixel shift +0.5 was fixed #raise RuntimeError('check') if sectWise: subpxl[-2:] = N.where(windowSize[-2:] > 1, subpxl[-2:]-0.5, subpxl[-2:]) else: subpxl[-n:] = N.where(windowSize[-n:] > 1, subpxl[-n:]-0.5, subpxl[-n:]) canvas = U.nd.shift(canvas, subpxl) canvas = canvas[slc_edge] check = 1 except IndexError: print('position ', pos, ' was skipped') check = 0 raise if check: arrList += [N.ascontiguousarray(canvas)] return arrList
def pointsCutOutND(arr, posList, windowSize=100, sectWise=None, interpolate=True): """ array: nd array posList: ([(z,)y,x]...) windowSize: scalar (in pixel or as percent < 1.) or ((z,)y,x) if arr.ndim > 2, and len(windowSize) == 2, then cut out section-wise (higher dimensions stay the same) sectWise: conern only XY of windowSize (higher dimensions stay the same) interpolate: shift array by subpixel interpolation to adjust center return: list of array centered at each pos in posList """ shape = N.array(arr.shape) center = shape / 2. # prepare N-dimensional window size try: len(windowSize) # seq if sectWise: windowSize = windowSize[-2:] if len(windowSize) != arr.ndim: dim = len(windowSize) windowSize = tuple(shape[:-dim]) + tuple(windowSize) except TypeError: # scaler if windowSize < 1 and windowSize > 0: # percentage w = shape * windowSize if sectWise: w[:-2] = shape[:-2] windowSize = w.astype(N.uint16) else: windowSize = N.where(shape >= windowSize, windowSize, shape) if sectWise: windowSize = arr.shape[:-2] + windowSize[-2:] windowSize = N.asarray(windowSize) # cutout individual position arrList = [] for pos in posList: # prepare N-dimensional coordinate n = len(pos) if n != len(windowSize): temp = center.copy() center[-n:] = pos pos = center # calculate idx ori = pos - (windowSize / 2.) # float value oidx = N.ceil(ori) # idx subpxl = oidx - ori # subpixel mod if interpolate and N.sometrue(subpxl): # comit to make shift SHIFT = 1 else: SHIFT = 0 # prepare slice # when comitted to make shift, first cut out window+1, # then make subpixle shift, and then cutout 1 edge slc = [Ellipsis] # Ellipsis is unnecessary, just in case... slc_edge = [slice(1, -1, None)] * arr.ndim for d in range(arr.ndim): start = oidx[d] - SHIFT if start < 0: start = 0 slc_edge[d] = slice(0, slc_edge[d].stop, None) stop = oidx[d] + windowSize[d] + SHIFT if stop > shape[d]: stop = shape[d] slc_edge[d] = slice(slc_edge[d].start, shape[d], None) slc += [slice(int(start), int(stop), None)] # cutout, shift and cutout try: canvas = arr[slc] if SHIFT: canvas = U.nd.shift(canvas, subpxl) canvas = canvas[slc_edge] check = 1 except IndexError: print('position ', pos, ' was skipped') check = 0 raise if check: arrList += [N.ascontiguousarray(canvas)] return arrList
def getShift(shift, ZYX, erosionZYX=0): """ shift: zyxrmm return [zmin,zmax,ymin,ymax,xmin,xmax] """ # erosion try: if len(erosionZYX) == 3: erosionZ = erosionZYX[0] erosionYX = erosionZYX[1:] elif len(erosionZYX) == 2: erosionZ = 0 erosionYX = erosionZYX elif len(erosionZYX) == 1: erosionZ = 0 erosionYX = erosionZYX[0] except TypeError: # scalar erosionZ = erosionZYX erosionYX = erosionZYX # magnification magZYX = N.ones((3, ), N.float32) magZYX[3 - len(shift[4:]):] = shift[4:] if len(shift[4:]) == 1: magZYX[1] = shift[4] # rotation r = shift[3] # target shape ZYX = N.asarray(ZYX, N.float32) ZYXm = ZYX * magZYX # Z z = N.where(shift[0] < 0, N.floor(shift[0]), N.ceil(shift[0])) ztop = ZYXm[0] + z nz = N.ceil(N.where(ztop > ZYX[0], ZYX[0], ztop)) z += erosionZ nz -= erosionZ if z < 0: z = 0 if nz < 0: nz = z + 1 zyx0 = N.ceil((ZYX - ZYXm) / 2.) #print zyx0 #if zyx0[0] > 0: # z -= zyx0[0] # nz += zyx0[0] zs = N.array([z, nz]) # YX #try: # if len(erosionYX) != 2: # raise ValueError, 'erosion is only applied to lateral dimension' #except TypeError: # erosionYX = (erosionYX, erosionYX) yxShift = N.where(shift[1:3] < 0, N.floor(shift[1:3]), N.ceil(shift[1:3])) # rotate the magnified center xyzm = N.ceil(ZYXm[::-1]) / 2. xyr = imgGeo.RotateXY(xyzm[:-1], r) xyr -= xyzm[:-1] yx = xyr[::-1] leftYX = N.ceil(N.abs(yx)) rightYX = -N.ceil(N.abs(yx)) # then translate leftYXShift = (leftYX + yxShift) + zyx0[1:] leftYXShift = N.where(leftYXShift < 0, 0, leftYXShift) rightYXShift = (rightYX + yxShift) - zyx0[1:] YXmax = N.where(ZYXm[1:] > ZYX[1:], ZYXm[1:], ZYX[1:]) rightYXShift = N.where(rightYXShift > 0, YXmax, rightYXShift + YXmax) # deal with - idx rightYXShift = N.where(rightYXShift > ZYX[1:], ZYX[1:], rightYXShift) leftYXShift += erosionYX rightYXShift -= erosionYX # (z0,z1,y0,y1,x0,x1) tempZYX = N.array( (zs[0], zs[1], int(N.ceil(leftYXShift[0])), int(rightYXShift[0]), int(N.ceil(leftYXShift[1])), int(rightYXShift[1]))) return tempZYX
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 trans3D_affine(arr, tzyx=(0,0,0), r=0, mag=1, dzyx=(0,0,0), rzy=0, ncpu=NCPU, order=ORDER):#**kwds): """ return array """ dtype = arr.dtype.type arr = arr.astype(N.float32) ndim = arr.ndim if ndim == 2: arr = arr.reshape((1,)+arr.shape) elif ndim == 3: if len(tzyx) < ndim: tzyx = (0,)*(ndim-len(tzyx)) + tuple(tzyx) if len(dzyx) < ndim: dzyx = (0,)*(ndim-len(dzyx)) + tuple(dzyx) dzyx = N.asarray(dzyx) magz = 1 try: if len(mag) == 3: magz = mag[0] mag = mag[1:] except TypeError: pass if ndim == 3 and (magz != 1 or tzyx[-3] or rzy): #print magz, arr.shape # because, mergins introduced after 2D transformation may interfere the result of this vertical transform, vertical axis was processed first, since rzy is 0 usually. arrT = arr.T # zyx -> xyz magzz = (1,magz) canvas = N.zeros_like(arrT) tzy = (0,tzyx[-3]) dzy = (dzyx[-2], dzyx[-3]) #if ncpu > 1 and mp: ret = ppro.pmap(_dothat, arrT, ncpu, tzy, rzy, magzz, dzy, order) for x, a in enumerate(ret): canvas[x] = a #else: # for x, a in enumerate(arrT): # canvas[x] = _dothat(a, tzy, rzy, magzz, dzy, order) arr = canvas.T #del arrT if N.any(tzyx[-2:]) or r or N.any(mag): #print ndim, arr.shape canvas = N.zeros_like(arr) if ndim == 3:# and ncpu > 1 and mp: # dividing XY into pieces did not work for rotation and magnification # here parallel processing is done section-wise since affine works only for 2D ret = ppro.pmap(_dothat, arr, ncpu, tzyx[-2:], r, mag, dzyx[-2:], order) for z, a in enumerate(ret): canvas[z] = a else: for z, a in enumerate(arr): canvas[z] = _dothat(a, tzyx[-2:], r, mag, dzyx[-2:], order) if ndim == 2: canvas = canvas[0] arr = canvas if dtype in (N.int, N.uint8, N.uint16, N.uint32): arr = N.where(arr < 0, 0, arr) return arr.astype(dtype)
def trans3D_spline(a, tzyx=(0,0,0), r=0, mag=1, dzyx=(0,0), rzy=0, mr=0, reshape=False, ncpu=1, **splinekwds): """ mag: scalar_for_yx or [y,x] or [z,y,x] mr: rotational direction of yx-zoom in degrees ncpu: no usage """ splinekwds['prefilter'] = splinekwds.get('prefilter', True) splinekwds['order'] = splinekwds.get('order', 3) ndim = a.ndim shape = N.array(a.shape, N.float32) tzyx = N.asarray(tzyx, N.float32) # rotation axis if ndim == 3: axes = (1,2) else: axes = (1,0) # magnification try: if len(mag) == 1: # same as scalar mag = [1] * (ndim-2) + list(mag) * 2 else: mag = [1] * (ndim-2) * (3-len(mag)) + list(mag) except: # scalar -> convert to yx mag only mag = [1] * (ndim-2) + ([mag] * ndim)[:2] mag = N.asarray(mag) try: dzyx = N.array([0] * (ndim-2) * (3-len(dzyx)) + list(dzyx)) except: # scalar pass if mr: a = U.nd.rotate(a, mr, axes=axes, reshape=reshape, **splinekwds) splinekwds['prefilter'] = False if N.any(dzyx): a = U.nd.shift(a, -dzyx, **splinekwds) splinekwds['prefilter'] = False if r: a = U.nd.rotate(a, -r, axes=axes, reshape=reshape, **splinekwds) splinekwds['prefilter'] = False if N.any(mag != 1): a = U.nd.zoom(a, zoom=mag, **splinekwds) splinekwds['prefilter'] = False if not reshape: dif = (shape - N.array(a.shape, N.float32)) / 2. mod = N.ceil(N.mod(dif, 1)) tzyx[-ndim:] -= (mod / 2.) if rzy and ndim >= 3: # is this correct?? havn't tried yet a = U.nd.rotate(a, -rzy, axes=(0,1), reshape=reshape, **splinekwds) if N.any(dzyx): a = U.nd.shift(a, dzyx, **splinekwds) if mr: a = U.nd.rotate(a, -mr, axes=axes, reshape=reshape, **splinekwds) if reshape: a = U.nd.shift(a, tzyx[-ndim:], **splinekwds) else: tzyx0 = N.where(mag >= 1, tzyx[-ndim:], 0) if N.any(tzyx0[-ndim:]): a = U.nd.shift(a, tzyx0[-ndim:], **splinekwds) if N.any(mag != 1) and not reshape: a = keepShape(a, shape, (dif, mod)) old=""" canvas = N.zeros(shape, a.dtype.type) #dif = (shape - N.array(a.shape, N.float32)) / 2 #mod = N.ceil(N.mod(dif, 1)) dif = N.where(dif > 0, N.ceil(dif), N.floor(dif)) # smaller aoff = N.where(dif < 0, 0, dif) aslc = [slice(dp, shape[i]-dp+mod[i]) for i, dp in enumerate(aoff)] # larger coff = N.where(dif > 0, 0, -dif) cslc = [slice(dp, a.shape[i]-dp+mod[i]) for i, dp in enumerate(coff)] canvas[aslc] = a[cslc] a = canvas""" if not reshape: tzyx0 = N.where(mag < 1, tzyx[-ndim:], 0) if N.any(mag != 1): tzyx0[-ndim:] -= (mod / 2.) if N.any(tzyx0[-ndim:]): a = U.nd.shift(a, tzyx0[-ndim:], **splinekwds) return a
def arr_log(arr): logArr = N.log(arr) return N.where(logArr < 0, 0, logArr)
def trans3D_spline(a, tzyx=(0, 0, 0), r=0, mag=1, dzyx=(0, 0), rzy=0, mr=0, reshape=False, ncpu=1, **splinekwds): """ mag: scalar_for_yx or [y,x] or [z,y,x] mr: rotational direction of yx-zoom in degrees ncpu: no usage """ splinekwds['prefilter'] = splinekwds.get('prefilter', True) splinekwds['order'] = splinekwds.get('order', 3) ndim = a.ndim shape = N.array(a.shape, N.float32) tzyx = N.asarray(tzyx, N.float32) # rotation axis if ndim == 3: axes = (1, 2) else: axes = (1, 0) # magnification try: if len(mag) == 1: # same as scalar mag = [1] * (ndim - 2) + list(mag) * 2 else: mag = [1] * (ndim - 2) * (3 - len(mag)) + list(mag) except: # scalar -> convert to yx mag only mag = [1] * (ndim - 2) + ([mag] * ndim)[:2] mag = N.asarray(mag) try: dzyx = N.array([0] * (ndim - 2) * (3 - len(dzyx)) + list(dzyx)) except: # scalar pass if mr: a = U.nd.rotate(a, mr, axes=axes, reshape=reshape, **splinekwds) splinekwds['prefilter'] = False if N.any(dzyx): a = U.nd.shift(a, -dzyx, **splinekwds) splinekwds['prefilter'] = False if r: a = U.nd.rotate(a, -r, axes=axes, reshape=reshape, **splinekwds) splinekwds['prefilter'] = False if N.any(mag != 1): a = U.nd.zoom(a, zoom=mag, **splinekwds) splinekwds['prefilter'] = False if not reshape: dif = (shape - N.array(a.shape, N.float32)) / 2. mod = N.ceil(N.mod(dif, 1)) tzyx[-ndim:] -= (mod / 2.) if rzy and ndim >= 3: # is this correct?? havn't tried yet a = U.nd.rotate(a, -rzy, axes=(0, 1), reshape=reshape, **splinekwds) if N.any(dzyx): a = U.nd.shift(a, dzyx, **splinekwds) if mr: a = U.nd.rotate(a, -mr, axes=axes, reshape=reshape, **splinekwds) if reshape: a = U.nd.shift(a, tzyx[-ndim:], **splinekwds) else: tzyx0 = N.where(mag >= 1, tzyx[-ndim:], 0) if N.any(tzyx0[-ndim:]): a = U.nd.shift(a, tzyx0[-ndim:], **splinekwds) if N.any(mag != 1) and not reshape: a = keepShape(a, shape, (dif, mod)) old = """ canvas = N.zeros(shape, a.dtype.type) #dif = (shape - N.array(a.shape, N.float32)) / 2 #mod = N.ceil(N.mod(dif, 1)) dif = N.where(dif > 0, N.ceil(dif), N.floor(dif)) # smaller aoff = N.where(dif < 0, 0, dif) aslc = [slice(dp, shape[i]-dp+mod[i]) for i, dp in enumerate(aoff)] # larger coff = N.where(dif > 0, 0, -dif) cslc = [slice(dp, a.shape[i]-dp+mod[i]) for i, dp in enumerate(coff)] canvas[aslc] = a[cslc] a = canvas""" if not reshape: tzyx0 = N.where(mag < 1, tzyx[-ndim:], 0) if N.any(mag != 1): tzyx0[-ndim:] -= (mod / 2.) if N.any(tzyx0[-ndim:]): a = U.nd.shift(a, tzyx0[-ndim:], **splinekwds) return a
def trans3D_affine(arr, tzyx=(0, 0, 0), r=0, mag=1, dzyx=(0, 0, 0), rzy=0, ncpu=NCPU, order=ORDER): #**kwds): """ return array """ dtype = arr.dtype.type arr = arr.astype(N.float32) ndim = arr.ndim if ndim == 2: arr = arr.reshape((1, ) + arr.shape) elif ndim == 3: if len(tzyx) < ndim: tzyx = (0, ) * (ndim - len(tzyx)) + tuple(tzyx) if len(dzyx) < ndim: dzyx = (0, ) * (ndim - len(dzyx)) + tuple(dzyx) dzyx = N.asarray(dzyx) magz = 1 try: if len(mag) == 3: magz = mag[0] mag = mag[1:] except TypeError: pass if ndim == 3 and (magz != 1 or tzyx[-3] or rzy): #print magz, arr.shape # because, mergins introduced after 2D transformation may interfere the result of this vertical transform, vertical axis was processed first, since rzy is 0 usually. arrT = arr.T # zyx -> xyz magzz = (1, magz) canvas = N.zeros_like(arrT) tzy = (0, tzyx[-3]) dzy = (dzyx[-2], dzyx[-3]) #if ncpu > 1 and mp: ret = ppro.pmap(_dothat, arrT, ncpu, tzy, rzy, magzz, dzy, order) for x, a in enumerate(ret): canvas[x] = a #else: # for x, a in enumerate(arrT): # canvas[x] = _dothat(a, tzy, rzy, magzz, dzy, order) arr = canvas.T #del arrT if N.any(tzyx[-2:]) or r or N.any(mag): #print ndim, arr.shape canvas = N.zeros_like(arr) if ndim == 3: # and ncpu > 1 and mp: # dividing XY into pieces did not work for rotation and magnification # here parallel processing is done section-wise since affine works only for 2D ret = ppro.pmap(_dothat, arr, ncpu, tzyx[-2:], r, mag, dzyx[-2:], order) for z, a in enumerate(ret): canvas[z] = a else: for z, a in enumerate(arr): canvas[z] = _dothat(a, tzyx[-2:], r, mag, dzyx[-2:], order) if ndim == 2: canvas = canvas[0] arr = canvas if dtype in (N.int, N.uint8, N.uint16, N.uint32): arr = N.where(arr < 0, 0, arr) return arr.astype(dtype)
def rotateIndicesND(slicelist, dtype=N.float64, rot=0, mode=2): """ slicelist: even shape works much better than odd shape rot: counter-clockwise, xy-plane mode: testing different ways of doing, (1 or 2 and the same result) return inds, LD """ global INDS_DIC shape = [] LD = [] for sl in slicelist: if isinstance(sl, slice): shape.append(sl.stop - sl.start) LD.append(sl.start) shapeTuple = tuple(shape+[rot]) if shapeTuple in INDS_DIC: inds = INDS_DIC[shapeTuple] else: shape = N.array(shape) ndim = len(shape) odd_even = shape % 2 s2 = N.ceil(shape * (2**0.5)) if mode == 1: # everything is even s2 = N.where(s2 % 2, s2 + 1, s2) elif mode == 2: # even & even or odd & odd for d, s in enumerate(shape): if (s % 2 and not s2[d] % 2) or (not s % 2 and s2[d] % 2): s2[d] += 1 cent = s2 / 2. dif = (s2 - shape) / 2. dm = dif % 1 # print s2, cent, dif, dm slc = [Ellipsis] + [slice(int(d), int(d)+shape[i]) for i, d in enumerate(dif)] # This slice is float which shift array when cutting out!! s2 = tuple([int(ss) for ss in s2]) # numpy array cannot use used for slice inds = N.indices(s2, N.float32) ind_shape = inds.shape nz = N.product(ind_shape[:-2]) nsec = nz / float(ndim) if ndim > 2: inds = N.reshape(inds, (nz,)+ind_shape[-2:]) irs = N.empty_like(inds) for d, ind in enumerate(inds): idx = int(d//nsec) c = cent[idx] if rot and inds.ndim > 2: U.trans2d(ind - c, irs[d], (0,0,rot,1,0,1)) irs[d] += c - dif[idx] else: irs[d] = ind - dif[idx] if len(ind_shape) > 2: irs = N.reshape(irs, ind_shape) irs = irs[slc] if mode == 1 and N.sometrue(dm): inds = N.empty_like(irs) # print 'translate', dm for d, ind in enumerate(irs): U.trans2d(ind, inds[d], (-dm[1], -dm[0], 0, 1, 0, 1)) else: inds = irs INDS_DIC[shapeTuple] = inds r_inds = N.empty_like(inds) for d, ld in enumerate(LD): r_inds[d] = inds[d] + ld return r_inds, LD