def reconstruct(sname, rot_center, ovlpfind, s_start, s_end): fname = dfolder + sname + '.h5' print(fname) start = s_start end = s_end chunks = 24 num_sino = (end - start) // chunks for m in range(chunks): sino_start = start + num_sino * m sino_end = start + num_sino * (m + 1) start_read_time = time.time() proj, flat, dark, thetat = dxchange.read_aps_2bm(fname, sino=(sino_start, sino_end)) print(' done read in %0.1f min' % ((time.time() - start_read_time) / 60)) dark = proj[9001:9002] flat = proj[0:1] proj = proj[1:9000] theta = tomopy.angles(proj.shape[0], 0., 360.) proj = tomopy.sino_360_to_180(proj, overlap=ovlpfind, rotation='right') proj = tomopy.remove_outlier(proj, dif=0.4) proj = tomopy.normalize_bg(proj, air=10) proj = tomopy.minus_log(proj) center = rot_center start_ring_time = time.time() proj = tomopy.remove_stripe_fw(proj, wname='sym5', sigma=4, pad=False) proj = tomopy.remove_stripe_sf(proj, size=3) print(' done pre-process in %0.1f min' % ((time.time() - start_ring_time) / 60)) start_phase_time = time.time() proj = tomopy.retrieve_phase(proj, pixel_size=detector_pixel_size_x, dist=sample_detector_distance, energy=energy, alpha=alpha, pad=True, ncore=None, nchunk=None) print(' done phase retrieval in %0.1f min' % ((time.time() - start_phase_time) / 60)) start_recon_time = time.time() rec = tomopy.recon(proj, theta, center=center, algorithm='gridrec', filter_name='ramalk') tomopy.circ_mask(rec, axis=0, ratio=0.95) print("Reconstructed", rec.shape) dxchange.write_tiff_stack(rec, fname=dfolder + '/' + sname + '/' + sname, overwrite=True, start=sino_start) print(' Chunk reconstruction done in %0.1f min' % ((time.time() - start_recon_time) / 60)) print("Done!")
def rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning): zinger_level = 800 # Zinger level for projections zinger_level_w = 1000 # Zinger level for white data_shape = get_dx_dims(h5fname, 'data') print(data_shape) ssino = int(data_shape[1] * nsino) center_range = (rot_center-center_search_width, rot_center+center_search_width, 0.5) #print(sino,ssino, center_range) #print(center_range[0], center_range[1], center_range[2]) # Select sinogram range to reconstruct sino = None start = ssino end = start + 1 sino = (start, end) # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) # zinger_removal proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0) flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0) # Flat-field correction of raw data. data = tomopy.normalize(proj, flat, dark, cutoff=1.4) # remove stripes data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) stack = np.empty((len(np.arange(*center_range)), data_shape[0], data_shape[2])) index = 0 for axis in np.arange(*center_range): stack[index] = data[:, 0, :] index = index + 1 # Reconstruct the same slice with a range of centers. rec = tomopy.recon(stack, theta, center=np.arange(*center_range), sinogram_order=True, algorithm='gridrec', filter_name='parzen', nchunk=1) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) index = 0 # Save images to a temporary folder. fname = os.path.dirname(h5fname) + '/' + 'try_rec/' + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0] for axis in np.arange(*center_range): rfname = fname + '_' + str('{0:.2f}'.format(axis) + '.tiff') dxchange.write_tiff(rec[index], fname=rfname, overwrite=True) index = index + 1 print("Reconstructions: ", fname)
def main(arg): fname = '/local/dataraid/elettra/Oak_16bit_slice343_all_repack.h5' # Read the hdf raw data. sino, sflat, sdark, th = dxchange.read_aps_32id(fname) slider(sino) # Set data collection angles as equally spaced between 0-180 degrees. theta = tomopy.angles(sino.shape[1], ang1=0.0, ang2=180.0) print(sino.shape, sdark.shape, sflat.shape, theta.shape) # Quick normalization just to see something .... ndata = sino / float(np.amax(sino)) slider(ndata) # Find rotation center. rot_center = 962 binning = 1 ndata = tomopy.downsample(ndata, level=int(binning)) rot_center = rot_center/np.power(2, float(binning)) ndata = tomopy.minus_log(ndata) # Reconstruct object using Gridrec algorithm. rec = tomopy.recon(ndata, theta, center=rot_center, sinogram_order=True, algorithm='gridrec') # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) # Write data as stack of TIFs. dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'): sample_detector_distance = 3 # Propagation distance of the wavefront in cm detector_pixel_size_x = 1.17e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4) monochromator_energy = 22.7 # Energy of incident wave in keV alpha = 1e-02 # Phase retrieval coeff. zinger_level = 800 # Zinger level for projections zinger_level_w = 1000 # Zinger level for white # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) # zinger_removal proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0) flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0) # Flat-field correction of raw data. ##data = tomopy.normalize(proj, flat, dark, cutoff=0.8) data = tomopy.normalize(proj, flat, dark) # remove stripes ## data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) ##data = tomopy.remove_stripe_ti(data, alpha=1.5) data = tomopy.remove_stripe_sf(data, size=150) # phase retrieval ##data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 rot_center = rot_center / np.power(2, float(binning)) data = tomopy.downsample(data, level=binning) data = tomopy.downsample(data, level=binning, axis=1) # Reconstruct object. if algorithm == 'sirtfbp': rec = rec_sirtfbp(data, theta, rot_center) else: rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen') print("Algorithm: ", algorithm) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) return rec
def recon_gridrec(sinogram, center, angles=None, ratio=1.0): """ Wrapper of the gridrec method implemented in the tomopy package. https://tomopy.readthedocs.io/en/latest/api/tomopy.recon.algorithm.html Parameters ---------- sinogram : array_like 2D tomographic data. center : float Center of rotation. angles : float 1D array. Tomographic angles in radian. ratio : float To apply a circle mask to the reconstructed image. Returns ------- ndarray Square array. """ (nrow, _) = sinogram.shape if angles is None: angles = np.linspace(0.0, 180.0, nrow) * np.pi / 180.0 sinogram = np.expand_dims(sinogram, 1) recont = tomopy.recon(sinogram, angles, center=center, algorithm='gridrec') recont = tomopy.circ_mask(recont, axis=0, ratio=ratio) return recont[0]
def _adjust_hist_limits(rec, mask, ratio): # Apply circular mask. if mask is True: rec = tomopy.circ_mask(rec, axis=0, ratio=ratio) # Adjust histogram boundaries according to reconstruction. return _adjust_hist_min(rec.min()), _adjust_hist_max(rec.max())
def reconstruct_tomcat(proj, angle_offset, rot_center): theta = tomopy.angles(proj.shape[0]) + angle_offset recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec') # rot_center = tomopy.find_center(proj, theta, init=882, ind=0, tol=0.5) print(f"Reconstruction shape: {recon.shape}") recon = tomopy.circ_mask(recon, axis=0, ratio=0.90) return recon
def rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning): data_shape = get_dx_dims(h5fname, 'data') print(data_shape) ssino = int(data_shape[1] * nsino) center_range = (rot_center-center_search_width, rot_center+center_search_width, 0.5) #print(sino,ssino, center_range) #print(center_range[0], center_range[1], center_range[2]) # Select sinogram range to reconstruct sino = None start = ssino end = start + 1 sino = (start, end) # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) # Flat-field correction of raw data. data = tomopy.normalize(proj, flat, dark, cutoff=1.4) # remove stripes # data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 stack = np.empty((len(np.arange(*center_range)), data_shape[0], data_shape[2])) index = 0 for axis in np.arange(*center_range): stack[index] = data[:, 0, :] index = index + 1 # Reconstruct the same slice with a range of centers. rec = tomopy.recon(stack, theta, center=np.arange(*center_range), sinogram_order=True, algorithm='gridrec', filter_name='parzen', nchunk=1) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) index = 0 # Save images to a temporary folder. fname = os.path.dirname(h5fname) + os.sep + 'try_rec/' + path_base_name(h5fname) + os.sep + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0] for axis in np.arange(*center_range): rfname = fname + '_' + str('{0:.2f}'.format(axis) + '.tiff') dxchange.write_tiff(rec[index], fname=rfname, overwrite=True) index = index + 1 print("Reconstructions: ", fname)
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'): sample_detector_distance = 8 # Propagation distance of the wavefront in cm detector_pixel_size_x = 2.247e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4) monochromator_energy = 24.9 # Energy of incident wave in keV alpha = 1e-02 # Phase retrieval coeff. zinger_level = 800 # Zinger level for projections zinger_level_w = 1000 # Zinger level for white # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) # zinger_removal # proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0) # flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0) # Flat-field correction of raw data. ##data = tomopy.normalize(proj, flat, dark, cutoff=0.8) data = tomopy.normalize(proj, flat, dark) # remove stripes data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) # data = tomopy.remove_stripe_ti(data, alpha=1.5) # data = tomopy.remove_stripe_sf(data, size=150) # phase retrieval #data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 rot_center = rot_center/np.power(2, float(binning)) data = tomopy.downsample(data, level=binning) data = tomopy.downsample(data, level=binning, axis=1) # Reconstruct object. if algorithm == 'sirtfbp': rec = rec_sirtfbp(data, theta, rot_center) elif algorithm == 'astrasirt': extra_options ={'MinConstraint':0} options = {'proj_type':'cuda', 'method':'SIRT_CUDA', 'num_iter':200, 'extra_options':extra_options} rec = tomopy.recon(data, theta, center=rot_center, algorithm=tomopy.astra, options=options) else: rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen') print("Algorithm: ", algorithm) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) return rec
def main(): # file_name = '/local/data/2020-02/Stock/100_B949_81_84_B2.h5' file_name = '/local/data/tomo_00001.h5' data_size = get_dx_dims(file_name) ssino = int(data_size[1] * 0.5) detector_center = int(data_size[2] * 0.5) # Select sinogram range to reconstruct sino_start = ssino sino_end = sino_start + 1 sino = (int(sino_start), int(sino_end)) # Read APS 2-BM raw data proj, flat, dark, theta = dxchange.read_aps_32id(file_name, sino=sino) tomo_ind = tomopy.normalize(proj, flat, dark) # data = tomopy.normalize_bg(proj, air=10) tomo_ind = tomopy.minus_log(tomo_ind) rec = recon(tomo_ind, theta, center=detector_center, sinogram_order=False, algorithm='gridrec', filter_name='shepp') rec = circ_mask(rec, axis=0) # tomopy score, simplified rescaling of histogram bounds hmin, hmax = _adjust_hist_limits( tomo_ind, theta, mask=True, sinogram_order=False) print(hmin, hmax) print(find_center(tomo_ind, theta)) centers = np.linspace(-25, 25, 101) + detector_center print(centers) tpscore = [] blur = [] for center in centers: val, bval = _find_center_cost( center, tomo_ind, theta, hmin, hmax, mask=True, ratio=1., sinogram_order=False) tpscore.append(val) blur.append(bval) plt.plot(centers, rescale(tpscore), label='tomopy score') plt.plot(centers, rescale(blur), label='blurriness') plt.legend() plt.title('centering scores') plt.xlabel('center') plt.show()
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'): sample_detector_distance = 8 # Propagation distance of the wavefront in cm detector_pixel_size_x = 2.247e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4) monochromator_energy = 24.9 # Energy of incident wave in keV alpha = 1e-02 # Phase retrieval coeff. zinger_level = 800 # Zinger level for projections zinger_level_w = 1000 # Zinger level for white # h5fname_norm = '/local/data/2019-02/Burke/C47M_0015.h5' h5fname_norm = '/local/data/2019-02/Burke/kc78_Menardii_0003.h5' proj1, flat, dark, theta1 = dxchange.read_aps_32id(h5fname_norm, sino=sino) proj, dummy, dummy1, theta = dxchange.read_aps_32id(h5fname, sino=sino) # zinger_removal proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0) flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0) # Flat-field correction of raw data. ##data = tomopy.normalize(proj, flat, dark, cutoff=0.8) data = tomopy.normalize(proj, flat, dark) # remove stripes data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) #data = tomopy.remove_stripe_ti(data, alpha=1.5) data = tomopy.remove_stripe_sf(data, size=20) # phase retrieval #data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 rot_center = rot_center/np.power(2, float(binning)) data = tomopy.downsample(data, level=binning) data = tomopy.downsample(data, level=binning, axis=1) # Reconstruct object. if algorithm == 'sirtfbp': rec = rec_sirtfbp(data, theta, rot_center) else: rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen') print("Algorithm: ", algorithm) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) return rec
def reconstruct(h5fname, sino, rot_center, args, blocked_views=None): # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) # Manage the missing angles: if blocked_views is not None: print("Blocked Views: ", blocked_views) proj = np.concatenate((proj[0:blocked_views[0], :, :], proj[blocked_views[1] + 1:-1, :, :]), axis=0) theta = np.concatenate( (theta[0:blocked_views[0]], theta[blocked_views[1] + 1:-1])) # Flat-field correction of raw data. data = tomopy.normalize(proj, flat, dark, cutoff=1.4) # remove stripes data = tomopy.remove_stripe_fw(data, level=7, wname='sym16', sigma=1, pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 algorithm = args.algorithm ncores = args.ncores nitr = args.num_iter # always add algorithm _kwargs = {"algorithm": algorithm} # assign number of cores _kwargs["ncore"] = ncores # don't assign "num_iter" if gridrec or fbp if algorithm not in ["fbp", "gridrec"]: _kwargs["num_iter"] = nitr # Reconstruct object. with timemory.util.auto_timer( "[tomopy.recon(algorithm='{}')]".format(algorithm)): rec = tomopy.recon(proj, theta, **_kwargs) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) return rec
def main(arg): parser = argparse.ArgumentParser() parser.add_argument("top", help="top directory where the tiff images are located: /data/") parser.add_argument("start", nargs='?', const=1, type=int, default=1, help="index of the first image: 1000 (default 1)") args = parser.parse_args() top = args.top index_start = int(args.start) template = os.listdir(top)[0] nfile = len(fnmatch.filter(os.listdir(top), '*.tif')) index_end = index_start + nfile ind_tomo = range(index_start, index_end) fname = top + template print (nfile, index_start, index_end, fname) # Select the sinogram range to reconstruct. start = 0 end = 512 sino=(start, end) # Read the tiff raw data. ndata = dxchange.read_tiff_stack(fname, ind=ind_tomo, slc=(sino, None)) print(ndata.shape) binning = 8 ndata = tomopy.downsample(ndata, level=binning, axis=1) print(ndata.shape) # Normalize to 1 using the air counts ndata = tomopy.normalize_bg(ndata, air=5) ## slider(ndata) # Set data collection angles as equally spaced between 0-180 degrees. theta = tomopy.angles(ndata.shape[0]) rot_center = 960 print("Center of rotation: ", rot_center) ndata = tomopy.minus_log(ndata) # Reconstruct object using Gridrec algorithm. rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec') # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) # Write data as stack of TIFs. dxchange.write_tiff_stack(rec, fname='/local/dataraid/mark/rec/recon')
def reconstruct(self, sino, cors, angles, vol_shape, init): recon = tomopy.recon(sino, np.deg2rad(angles), center=cors[0], ncore=1, algorithm=self.alg, init_recon=init, **self.kwargs) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) return np.transpose(recon, (1, 0, 2))
def save_reconstruction(self, recon, savedir = None, index=-1): try: if savedir == "": raise IOError if savedir == None: savedir = QtGui.QFileDialog.getSaveFileName()[0] if index == -1: recon = tomopy.circ_mask(recon, axis=0) dxchange.writer.write_tiff_stack(recon, fname=savedir) if index != -1: recon = tomopy.circ_mask(recon, axis=0) indx = "0000" recon_index = indx[:-len(str(index))]+str(index) io.imsave(savedir+"_"+str(recon_index)+".tif", recon[0]) return except IOError: print("type the header name") except: print("Something went horribly wrong.")
def recon_wrapper(projs, beam, theta, pad_frac=0.8, mask_ratio=0.95, contrast_s=0.001): ''' Do a reconstruction with gridrec. Parameters ---------- projs : np.array a stack of radiographs (sinogram order input to tomopy recon is False) theta : tuple The tuple must be defined as (starting_theta, ending_theta, number_projections). The angle is intepreted as degrees. beam : np.array The flat-field (beam array) must be provided with shape (1, nrows, ncols). pad_frac : float Fraction of padding applied to rows (e.g. pad_frac = 0.8 on 1000 rows adds 800 pixels to either side. mask_ratio : float ratio between (0,1) for applying circular mask on reconstruction (typical value 0.9 to 1.0). ''' # make theta array in radians theta = np.linspace(*theta, endpoint=True) theta = np.radians(theta) projs = normalize(projs, beam, 1.0e-6 * np.zeros(beam.shape)) projs = minus_log(projs) pad_w = int(pad_frac * projs.shape[-1]) projs = np.pad(projs, ((0, 0), (0, 0), (pad_w, pad_w)), mode="constant", constant_values=0.0) rec = recon(projs, theta = theta, \ center = projs.shape[-1]//2, \ algorithm = 'gridrec', \ sinogram_order = False) rec = rec[:, pad_w:-pad_w, pad_w:-pad_w] rec = circ_mask(rec, 0, ratio=mask_ratio) mask_val = rec[int(rec.shape[0] // 2), 0, 0] vcrop = int(rec.shape[0] * (1 - mask_ratio)) rec[0:vcrop, ...] = mask_val rec[-vcrop:, ...] = mask_val if contrast_s > 0.0: h = modified_autocontrast(rec, s=contrast_s) rec = np.clip(rec, *h) return rec
def save_reconstruction(self, recon): try: savedir = QtGui.QFileDialog.getSaveFileName()[0] if savedir == "": raise IndexError recon = tomopy.circ_mask(recon, axis=0) dxchange.writer.write_tiff_stack(recon, fname=savedir) except IndexError: print("type the header name") return
def reconstruct(h5fname, sino, rot_center, args, blocked_views=None): # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) # Manage the missing angles: if blocked_views is not None: print("Blocked Views: ", blocked_views) proj = np.concatenate((proj[0:blocked_views[0], :, :], proj[blocked_views[1]+1:-1, :, :]), axis=0) theta = np.concatenate((theta[0:blocked_views[0]], theta[blocked_views[1]+1: -1])) # Flat-field correction of raw data. data = tomopy.normalize(proj, flat, dark, cutoff=1.4) # remove stripes data = tomopy.remove_stripe_fw(data, level=7, wname='sym16', sigma=1, pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 algorithm = args.algorithm ncores = args.ncores nitr = args.num_iter # always add algorithm _kwargs = {"algorithm": algorithm} # assign number of cores _kwargs["ncore"] = ncores # don't assign "num_iter" if gridrec or fbp if algorithm not in ["fbp", "gridrec"]: _kwargs["num_iter"] = nitr # Reconstruct object. with timemory.util.auto_timer( "[tomopy.recon(algorithm='{}')]".format(algorithm)): rec = tomopy.recon(proj, theta, **_kwargs) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) return rec
def save_reconstruction(self, recon, savedir=None): try: if savedir == "": raise IOError if savedir == None: savedir = QtGui.QFileDialog.getSaveFileName()[0] recon = tomopy.circ_mask(recon, axis=0) dxchange.writer.write_tiff_stack(recon, fname=savedir) return except IOError: print("type the header name") except: print("Something went horribly wrong.")
def _adjust_hist_limits(tomo_ind, theta, mask, sinogram_order): # Make an initial reconstruction to adjust histogram limits. rec = recon(tomo_ind, theta, sinogram_order=sinogram_order, algorithm='gridrec') # Apply circular mask. if mask is True: rec = circ_mask(rec, axis=0) # Adjust histogram boundaries according to reconstruction. return _adjust_hist_min(rec.min()), _adjust_hist_max(rec.max())
def mask(data, params): log.info(" *** mask") if(params.reconstruction_mask): log.info(' *** *** ON') if 0 < params.reconstruction_mask_ratio <= 1: log.warning(" *** mask ratio: %f " % params.reconstruction_mask_ratio) data = tomopy.circ_mask(data, axis=0, ratio=params.reconstruction_mask_ratio) log.info(' *** masking finished') else: log.error(" *** mask ratio must be between 0-1: %f is ignored" % params.reconstruction_mask_ratio) else: log.warning(' *** *** OFF') return data
def main(arg): parser = argparse.ArgumentParser() parser.add_argument( "top", help="top directory where the tiff images are located: /data/") parser.add_argument("start", nargs='?', const=1, type=int, default=1, help="index of the first image: 1000 (default 1)") args = parser.parse_args() top = args.top index_start = int(args.start) template = os.listdir(top)[0] nfile = len(fnmatch.filter(os.listdir(top), '*.tiff')) index_end = index_start + nfile ind_tomo = range(index_start, index_end) fname = top + template print(nfile, index_start, index_end, fname) # Select the sinogram range to reconstruct. start = 70 end = 72 sino = (start, end) # Read the tiff raw data. ndata = dxchange.read_tiff_stack(fname, ind=ind_tomo, slc=(sino, None)) # Set data collection angles as equally spaced between 0-180 degrees. theta = tomopy.angles(ndata.shape[0]) rot_center = 251 print("Center of rotation: ", rot_center) #ndata = tomopy.minus_log(ndata) # Reconstruct object using Gridrec algorithm. rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec') # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) # Write data as stack of TIFs. dxchange.write_tiff_stack(rec, fname='/local/dataraid/mark/rec/recon')
def save_recon_2npy(self,recon, savedir=None, index=-1): try: if savedir == "": raise IOError if savedir == None: savedir = QtGui.QFileDialog.getSaveFileName()[0] if index == -1: recon = tomopy.circ_mask(recon, axis=0) np.save(savedir, recon) return except IOError: print("type the header name") except: print("Something went horribly wrong.")
def _center_resid_blur(center, sino, omega, rmin, rmax, sinogram_order=True): """ Cost function used for the ``find_center`` routine. """ rec = tomopy.recon(sino, omega, center, sinogram_order=sinogram_order, algorithm='gridrec', filter_name='shepp') rec = tomopy.circ_mask(rec, axis=0) score = -((rec - rec.mean())**2).sum() logger.info("blur center = %.4f %.4f" % (center, score)) return score
def reconstruct(sname, rot_center, ovlpfind, s_start, s_end): fname = dfolder + sname + '.h5' print (fname) start = s_start end = s_end chunks = 24 num_sino = (end - start) // chunks for m in range(chunks): sino_start = start + num_sino * m sino_end = start + num_sino * (m + 1) start_read_time = time.time() proj, flat, dark, thetat = dxchange.read_aps_2bm(fname, sino=(sino_start, sino_end)) print(' done read in %0.1f min' % ((time.time() - start_read_time)/60)) dark = proj[9001:9002] flat = proj[0:1] proj = proj[1:9000] theta = tomopy.angles(proj.shape[0], 0., 360.) proj = tomopy.sino_360_to_180(proj, overlap=ovlpfind, rotation='right') proj = tomopy.remove_outlier(proj, dif=0.4) proj = tomopy.normalize_bg(proj, air=10) proj = tomopy.minus_log(proj) center = rot_center start_ring_time = time.time() proj = tomopy.remove_stripe_fw(proj, wname='sym5', sigma=4, pad=False) proj = tomopy.remove_stripe_sf(proj, size=3) print(' done pre-process in %0.1f min' % ((time.time() - start_ring_time)/60)) start_phase_time = time.time() proj = tomopy.retrieve_phase(proj, pixel_size=detector_pixel_size_x, dist=sample_detector_distance, energy=energy, alpha=alpha, pad=True, ncore=None, nchunk=None) print(' done phase retrieval in %0.1f min' % ((time.time() - start_phase_time)/60)) start_recon_time = time.time() rec = tomopy.recon(proj, theta, center=center, algorithm='gridrec', filter_name='ramalk') tomopy.circ_mask(rec, axis=0, ratio=0.95) print ("Reconstructed", rec.shape) dxchange.write_tiff_stack(rec, fname = dfolder + '/' + sname + '/' + sname, overwrite=True, start=sino_start) print(' Chunk reconstruction done in %0.1f min' % ((time.time() - start_recon_time)/60)) print ("Done!")
def img_variance(img): import tomopy s = img.shape variance = np.zeros(s[0]) img = tomopy.circ_mask(img, axis=0, ratio=0.8) for i in range(s[0]): img[i] = medfilt2d(img[i], 5) img_ = img[i].flatten() t = img_ > 0 img_ = img_[t] t = np.mean(img_) variance[i] = np.sqrt( np.sum(np.power(np.abs(img_ - t), 2)) / len(img_ - 1)) return variance
def recon_hdf5_mpi(src_fanme, dest_folder, sino_range, sino_step, center_vec, shift_grid, dtype='float32', algorithm='gridrec', tolerance=1, save_sino=False, sino_blur=None, **kwargs): """ Reconstruct a single tile, or fused HDF5 created using util/total_fusion. MPI supported. """ raise DeprecationWarning if rank == 0: if not os.path.exists(dest_folder): os.mkdir(dest_folder) sino_ini = int(sino_range[0]) sino_end = int(sino_range[1]) f = h5py.File(src_fanme) dset = f['exchange/data'] full_shape = dset.shape theta = tomopy.angles(full_shape[0]) center_vec = np.asarray(center_vec) sino_ls = np.arange(sino_ini, sino_end, sino_step, dtype='int') grid_bins = np.ceil(shift_grid[:, 0, 0]) t0 = time.time() alloc_set = allocate_mpi_subsets(sino_ls.size, size, task_list=sino_ls) for slice in alloc_set[rank]: print(' Rank {:d}: reconstructing {:d}'.format(rank, slice)) grid_line = np.digitize(slice, grid_bins) grid_line = grid_line - 1 center = center_vec[grid_line] data = dset[:, slice, :] if sino_blur is not None: data = gaussian_filter(data, sino_blur) data = data.reshape([full_shape[0], 1, full_shape[2]]) data[np.isnan(data)] = 0 data = data.astype('float32') if save_sino: dxchange.write_tiff(data[:, slice, :], fname=os.path.join(dest_folder, 'sino/recon_{:05d}_{:d}.tiff').format(slice, center)) # data = tomopy.remove_stripe_ti(data) rec = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs) # rec = tomopy.remove_ring(rec) rec = tomopy.remove_outlier(rec, tolerance) rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) dxchange.write_tiff(rec, fname='{:s}/recon/recon_{:05d}_{:d}'.format(dest_folder, slice, center), dtype=dtype) print('Rank {:d} finished in {:.2f} s.'.format(rank, time.time()-t0)) return
def rec(data, rot_center): """ Reconstruct with Gridrec. """ [nframes, nproj, ns, n] = data.shape theta = np.linspace(0, np.pi * nframes, nproj * nframes, endpoint=False) # Reconstruct object. FBP. rec = np.zeros([nframes, ns, n, n], dtype='float32') for time_frame in range(0, nframes): rec0 = tomopy.recon(data[time_frame], theta[time_frame * nproj:(time_frame + 1) * nproj], center=rot_center, algorithm='gridrec') # Mask each reconstructed slice with a circle. rec[time_frame] = tomopy.circ_mask(rec0, axis=0, ratio=0.95) return rec
def _find_center_cost(center, recon_function, hmin, hmax, mask, ratio, cache): """ Cost function used for the ``find_center`` routine. """ logger.info('Trying rotation center: %s', center) center = np.array(center, dtype='float32') if float(center) in cache: logger.info("Using cached value for center: %s", center) return cache[float(center)] rec = recon_function(center) if mask is True: rec = tomopy.circ_mask(rec, axis=0, ratio=ratio) hist, _ = np.histogram(rec, bins=64, range=[hmin, hmax]) hist = hist.astype('float32') / rec.size + 1e-12 val = -np.dot(hist, np.log2(hist)) logger.info("Function value = %f", val) cache[float(center)] = val return val
def _center_resid_negent(center, sino, omega, rmin, rmax, sinogram_order=True): """ Cost function used for the ``find_center`` routine. """ _, nang, nx = sino.shape if center < 1: return 10 * (1 - center) if center > nx - 2: return 10 * (center - nx + 2) n1 = int(nx / 4.0) n2 = int(3 * nx / 4.0) rec = tomopy.recon(sino, omega, center, algorithm='gridrec', sinogram_order=sinogram_order) rec = tomopy.circ_mask(rec, axis=0)[:, n1:n2, n1:n2] hist, e = np.histogram(rec, bins=64, range=[rmin, rmax]) hist = hist / rec.size score = -np.dot(hist, np.log(1.e-12 + hist)) logger.info("negent center = %.4f %.4f" % (center, score)) return score
def main(arg): fname = '/local/dataraid/elettra/Oak_16bit_slice343_all_repack.h5' # Read the hdf raw data. sino, sflat, sdark, th = dxchange.read_aps_32id(fname) slider(sino) proj = np.swapaxes(sino, 0, 1) flat = np.swapaxes(sflat, 0, 1) dark = np.swapaxes(sdark, 0, 1) # Set data collection angles as equally spaced between 0-180 degrees. theta = tomopy.angles(proj.shape[0], ang1=0.0, ang2=180.0) print(proj.shape, dark.shape, flat.shape, theta.shape) # Flat-field correction of raw data. ndata = tomopy.normalize(proj, flat, dark) #slider(ndata) # Find rotation center. rot_center = 962 binning = 1 ndata = tomopy.downsample(ndata, level=int(binning)) rot_center = rot_center / np.power(2, float(binning)) ndata = tomopy.minus_log(ndata) # Reconstruct object using Gridrec algorithm. rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec') # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) # Write data as stack of TIFs. dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def rec_tv(data, m, nsp, rot_center, lambda0, lambda1, niters, ngpus): """ Reconstruct. Time-domain decomposition + regularization. """ [nframes, nproj, ns, n] = data.shape # cut data according to the rotation center if (rot_center < n // 2): data = data[:, :, :, :n // 2 + rot_center - 1] if (rot_center > n // 2): data = data[:, :, :, rot_center - n // 2:] n = data.shape[3] # reorder input data for compatibility data = np.reshape(data, [nframes * nproj, ns, n]) data = np.ndarray.flatten(data.swapaxes(0, 1)) # memory for result rec = np.zeros([n * n * ns * m], dtype='float32') # Make a class for tv cl = rectv.rectv(n, nframes * nproj, m, nframes, ns, ns, ngpus, lambda0, lambda1) # Run iterations cl.itertvR_wrap(rec, data, niters) # reorder result for compatibility with tomopy rec = np.rot90(np.reshape(rec, [ns, m, n, n]).swapaxes(0, 1), axes=(2, 3)) / nproj * 2 # take slices corresponding to angles k\pi rec = rec[::m // nframes] for time_frame in range(0, nframes): # Mask each reconstructed slice with a circle. rec[time_frame] = tomopy.circ_mask(rec[time_frame], axis=0, ratio=0.95) return rec
def main(arg): fname = '/local/dataraid/elettra/Oak_16bit_slice343_all_repack.h5' # Read the hdf raw data. sino, sflat, sdark, th = dxchange.read_aps_32id(fname) slider(sino) proj = np.swapaxes(sino,0,1) flat = np.swapaxes(sflat,0,1) dark = np.swapaxes(sdark,0,1) # Set data collection angles as equally spaced between 0-180 degrees. theta = tomopy.angles(proj.shape[0], ang1=0.0, ang2=180.0) print(proj.shape, dark.shape, flat.shape, theta.shape) # Flat-field correction of raw data. ndata = tomopy.normalize(proj, flat, dark) #slider(ndata) # Find rotation center. rot_center = 962 binning = 1 ndata = tomopy.downsample(ndata, level=int(binning)) rot_center = rot_center/np.power(2, float(binning)) ndata = tomopy.minus_log(ndata) # Reconstruct object using Gridrec algorithm. rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec') # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) # Write data as stack of TIFs. dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def find_tomo_center(sino, omega, center=None, sinogram_order=True): xmax = sino.shape[0] if sinogram_order: xmax = sino.shape[1] if center is None: center = xmax / 2.0 # init center to scale recon rec = tomopy.recon(sino, omega, center=center, sinogram_order=sinogram_order, algorithm='gridrec', filter_name='shepp') rec = tomopy.circ_mask(rec, axis=0) # tomopy score, tweaked slightly rmin, rmax = rec.min(), rec.max() rmin -= 0.5 * (rmax - rmin) rmax += 0.5 * (rmax - rmin) out = minimize(_center_resid_negent, center, args=(sino, omega, rmin, rmax, sinogram_order), method='Nelder-Mead', tol=1.5) cen = out.x if cen > 0 and cen < xmax: out = minimize(_center_resid_blur, cen, args=(sino, omega, rmin, rmax, sinogram_order), method='Nelder-Mead', tol=0.5) cen = out.x return cen
def reconstruct(h5fname, sino, rot_center, blocked_views=None): # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) # Manage the missing angles: if blocked_views is not None: print("Blocked Views: ", blocked_views) proj = np.concatenate((proj[0:blocked_views[0],:,:], proj[blocked_views[1]+1:-1,:,:]), axis=0) theta = np.concatenate((theta[0:blocked_views[0]], theta[blocked_views[1]+1:-1])) # Flat-field correction of raw data. data = tomopy.normalize(proj, flat, dark, cutoff=1.4) # remove stripes data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) # # phase retrieval # data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=8e-3,pad=True) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 # Reconstruct object. rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec') # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) return rec
def _find_center_cost( center, tomo_ind, theta, hmin, hmax, mask, ratio, sinogram_order=False): """ Cost function used for the ``find_center`` routine. """ center = np.array(center, dtype='float32') rec = recon( tomo_ind, theta, center, sinogram_order=sinogram_order, algorithm='gridrec', filter_name='shepp') if mask is True: rec = circ_mask(rec, axis=0) hist, e = np.histogram(rec, bins=64, range=[hmin, hmax]) hist = hist.astype('float32') / rec.size + 1e-12 val = -np.dot(hist, np.log2(hist)) # from Matt bval = -((rec - rec.mean())**2).sum() return val, bval
def recon_hdf5(src_fanme, dest_folder, sino_range, sino_step, shift_grid, center_vec=None, center_eq=None, dtype='float32', algorithm='gridrec', tolerance=1, chunk_size=20, save_sino=False, sino_blur=None, flattened_radius=120, mode='180', test_mode=False, phase_retrieval=None, ring_removal=True, **kwargs): """ center_eq: a and b parameters in fitted center position equation center = a*slice + b. """ if not os.path.exists(dest_folder): try: os.mkdir(dest_folder) except: pass sino_ini = int(sino_range[0]) sino_end = int(sino_range[1]) sino_ls_all = np.arange(sino_ini, sino_end, sino_step, dtype='int') alloc_set = allocate_mpi_subsets(sino_ls_all.size, size, task_list=sino_ls_all) sino_ls = alloc_set[rank] # prepare metadata f = h5py.File(src_fanme) dset = f['exchange/data'] full_shape = dset.shape theta = tomopy.angles(full_shape[0]) if center_eq is not None: a, b = center_eq center_ls = sino_ls * a + b center_ls = np.round(center_ls) for iblock in range(int(sino_ls.size/chunk_size)+1): print('Beginning block {:d}.'.format(iblock)) t0 = time.time() istart = iblock*chunk_size iend = np.min([(iblock+1)*chunk_size, sino_ls.size]) fstart = sino_ls[istart] fend = sino_ls[iend] center = center_ls[istart:iend] data = dset[:, fstart:fend:sino_step, :] data[np.isnan(data)] = 0 data = data.astype('float32') data = tomopy.remove_stripe_ti(data, alpha=4) if sino_blur is not None: for i in range(data.shape[1]): data[:, i, :] = gaussian_filter(data[:, i, :], sino_blur) rec = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs) rec = tomopy.remove_ring(rec) rec = tomopy.remove_outlier(rec, tolerance) rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) for i in range(rec.shape[0]): slice = fstart + i*sino_step dxchange.write_tiff(rec[i, :, :], fname=os.path.join(dest_folder, 'recon/recon_{:05d}_{:05d}.tiff').format(slice, sino_ini)) if save_sino: dxchange.write_tiff(data[:, i, :], fname=os.path.join(dest_folder, 'sino/recon_{:05d}_{:d}.tiff').format(slice, int(center[i]))) iblock += 1 print('Block {:d} finished in {:.2f} s.'.format(iblock, time.time()-t0)) else: # divide chunks grid_bins = np.append(np.ceil(shift_grid[:, 0, 0]), full_shape[1]) chunks = [] center_ls = [] istart = 0 counter = 0 # irow should be 0 for slice 0 irow = np.searchsorted(grid_bins, sino_ls[0], side='right')-1 for i in range(sino_ls.size): counter += 1 sino_next = i+1 if i != sino_ls.size-1 else i if counter >= chunk_size or sino_ls[sino_next] >= grid_bins[irow+1] or sino_next == i: iend = i+1 chunks.append((istart, iend)) istart = iend center_ls.append(center_vec[irow]) if sino_ls[sino_next] >= grid_bins[irow+1]: irow += 1 counter = 0 # reconstruct chunks iblock = 1 for (istart, iend), center in izip(chunks, center_ls): print('Beginning block {:d}.'.format(iblock)) t0 = time.time() fstart = sino_ls[istart] fend = sino_ls[iend-1] print('Reading data...') data = dset[:, fstart:fend+1:sino_step, :] if mode == '360': overlap = 2 * (dset.shape[2] - center) data = tomosaic.morph.sino_360_to_180(data, overlap=overlap, rotation='right') theta = tomopy.angles(data.shape[0]) data[np.isnan(data)] = 0 data = data.astype('float32') if sino_blur is not None: for i in range(data.shape[1]): data[:, i, :] = gaussian_filter(data[:, i, :], sino_blur) if ring_removal: data = tomopy.remove_stripe_ti(data, alpha=4) if phase_retrieval: data = tomopy.retrieve_phase(data, kwargs['pixel_size'], kwargs['dist'], kwargs['energy'], kwargs['alpha']) rec0 = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs) rec = tomopy.remove_ring(np.copy(rec0)) cent = int((rec.shape[1]-1) / 2) xx, yy = np.meshgrid(np.arange(rec.shape[2]), np.arange(rec.shape[1])) mask0 = ((xx-cent)**2+(yy-cent)**2 <= flattened_radius**2) mask = np.zeros(rec.shape, dtype='bool') for i in range(mask.shape[0]): mask[i, :, :] = mask0 rec[mask] = (rec[mask] + rec0[mask])/2 else: rec = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs) rec = tomopy.remove_outlier(rec, tolerance) rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) for i in range(rec.shape[0]): slice = fstart + i*sino_step if test_mode: dxchange.write_tiff(rec[i, :, :], fname=os.path.join(dest_folder, 'recon/recon_{:05d}_{:d}.tiff').format(slice, center), dtype=dtype) else: dxchange.write_tiff(rec[i, :, :], fname=os.path.join(dest_folder, 'recon/recon_{:05d}.tiff').format(slice), dtype=dtype) if save_sino: dxchange.write_tiff(data[:, i, :], fname=os.path.join(dest_folder, 'sino/recon_{:05d}_{:d}.tiff').format(slice, center), dtype=dtype) print('Block {:d} finished in {:.2f} s.'.format(iblock, time.time()-t0)) iblock += 1 return
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'): sample_detector_distance = 31 # Propagation distance of the wavefront in cm detector_pixel_size_x = 1.17e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4) monochromator_energy = 65 # Energy of incident wave in keV # used pink beam alpha = 4*1e-4 # Phase retrieval coeff. zinger_level = 800 # Zinger level for projections zinger_level_w = 1000 # Zinger level for white # Read APS 2-BM raw data. # DIMAX saves 3 files: proj, flat, dark # when loading the data set select the proj file (larger size) fname = os.path.splitext(h5fname)[0] fbase = fname.rsplit('_', 1)[0] fnum = fname.rsplit('_', 1)[1] fext = os.path.splitext(h5fname)[1] fnum_flat = str("%4.4d" % (int(fnum)+1)) fnum_dark = str("%4.4d" % (int(fnum)+2)) fnproj = fbase + '_' + fnum + fext fnflat = fbase + '_' + fnum_flat + fext fndark = fbase + '_' + fnum_dark + fext print('proj', fnproj) print('flat', fnflat) print('dark', fndark) # Read APS 2-BM DIMAX raw data. proj, dum, dum2, theta = dxchange.read_aps_32id(fnproj, sino=sino) dum3, flat, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino) #flat, dum3, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino) dum6, dum7, dark, dum8 = dxchange.read_aps_32id(fndark, sino=sino) # Flat-field correction of raw data. data = tomopy.normalize(proj, flat, dark, cutoff=1.4) # remove stripes data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) # zinger_removal proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0) flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0) # Flat-field correction of raw data. ##data = tomopy.normalize(proj, flat, dark, cutoff=0.8) data = tomopy.normalize(proj, flat, dark) # remove stripes #data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) #data = tomopy.remove_stripe_ti(data, alpha=1.5) data = tomopy.remove_stripe_sf(data, size=150) # phase retrieval #data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 rot_center = rot_center/np.power(2, float(binning)) data = tomopy.downsample(data, level=binning) data = tomopy.downsample(data, level=binning, axis=1) # padding N = data.shape[2] data_pad = np.zeros([data.shape[0],data.shape[1],3*N//2],dtype = "float32") data_pad[:,:,N//4:5*N//4] = data data_pad[:,:,0:N//4] = np.tile(np.reshape(data[:,:,0],[data.shape[0],data.shape[1],1]),(1,1,N//4)) data_pad[:,:,5*N//4:] = np.tile(np.reshape(data[:,:,-1],[data.shape[0],data.shape[1],1]),(1,1,N//4)) data = data_pad rot_center = rot_center+N//4 nframes = 8 nproj = 1500 theta = np.linspace(0, np.pi*nframes, nproj*nframes, endpoint=False) rec = np.zeros( (nframes, data.shape[1], data.shape[2], data.shape[2]), dtype='float32') for time_frame in range(0, nframes): rec0 = tomopy.recon(data[time_frame*nproj:(time_frame+1)*nproj], theta[time_frame*nproj:( time_frame+1)*nproj], center=rot_center, algorithm='gridrec') # Mask each reconstructed slice with a circle. rec[time_frame] = tomopy.circ_mask(rec0, axis=0, ratio=0.95) rec = rec[:,:,N//4:5*N//4,N//4:5*N//4] print("Algorithm: ", algorithm) return rec
inputPath = '{}_{:d}{}'.format(fn,y,fileextension) tomo[y] = dxchange.reader.read_tiff(inputPath,slc = (sinoused, raysused)) print('loading flat images') for y in range(0,len(floc)): inputPath = '{}{}_{:d}{}'.format(fn,flatextension,floc[y],fileextension) flat[y] = dxchange.reader.read_tiff(inputPath,slc = (sinoused, raysused)) print('loading dark images') for y in range(0,numdrk): inputPath = '{}{}_{:d}{}'.format(fn,darkextension,y,fileextension) dark[y] = dxchange.reader.read_tiff(inputPath,slc = (sinoused, raysused)) print('normalizing') tomo = tomo.astype(np.float32) tomopy.normalize_nf(tomo, flat, dark, floc, out=tomo) tomopy.minus_log(tomo, out=tomo) tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge') rec = tomopy.recon(tomo, tomopy.angles(numangles, angle_offset, angle_offset-angularrange), center=cor+npad, algorithm='gridrec', filter_name='butterworth', filter_par=[.25, 2]) rec = rec[:, npad:-npad, npad:-npad] rec /= pxsize # convert reconstructed voxel values from 1/pixel to 1/cm rec = tomopy.circ_mask(rec, 0) print('writing recon') dxchange.write_tiff_stack(rec, fname='rec/'+fn, start=sinoused[0])
def reconstruct(filename,inputPath="", outputPath="", COR=COR, doOutliers=doOutliers, outlier_diff=outlier_diff, outlier_size=outlier_size, doFWringremoval=doFWringremoval, ringSigma=ringSigma,ringLevel=ringLevel, ringWavelet=ringWavelet,pad_sino=pad_sino, doPhaseRetrieval=doPhaseRetrieval, propagation_dist=propagation_dist, kev=kev,alphaReg=alphaReg, butterworthpars=butterworthpars, doPolarRing=doPolarRing,Rarc=Rarc, Rmaxwidth=Rmaxwidth, Rtmax=Rtmax, Rthr=Rthr, Rtmin=Rtmin, useAutoCOR=useAutoCOR, use360to180=use360to180, num_substacks=num_substacks,recon_slice=recon_slice): # Convert filename to list type if only one file name is given if type(filename) != list: filename=[filename] # If useAutoCor == true, a list of COR will be automatically calculated for all files # If a list of COR is given, only entries with boolean False will use automatic COR calculation if useAutoCOR==True or (len(COR) != len(filename)): logging.info('using auto COR for all input files') COR = [False]*len(filename) for x in range(len(filename)): logging.info('opening data set, checking metadata') fdata, gdata = read_als_832h5_metadata(inputPath[x]+filename[x]+'.h5') pxsize = float(gdata['pxsize'])/10.0 # convert from metadata (mm) to this script (cm) numslices = int(gdata['nslices']) # recon_slice == True, only center slice will be reconstructed # if integer is given, a specific if recon_slice != False: if (type(recon_slice) == int) and (recon_slice <= numslices): sinorange [recon_slice-1, recon_slice] else: sinorange = [numslices//2-1, numslices//2] else: sinorange = [0, numslices] # Calculate number of substacks (chunks) substacks = num_substacks #(sinorange[1]-sinorange[0]-1)//num_sino_per_substack+1 if (sinorange[1]-sinorange[0]) >= substacks: num_sino_per_substack = (sinorange[1]-sinorange[0])//num_substacks else: num_sino_per_substack = 1 firstcor, lastcor = 0, int(gdata['nangles'])-1 projs, flat, dark, floc = dxchange.read_als_832h5(inputPath[x]+filename[x]+'.h5', ind_tomo=(firstcor, lastcor)) projs = tomopy.normalize_nf(projs, flat, dark, floc) autocor = tomopy.find_center_pc(projs[0], projs[1], tol=0.25) if (type(COR[x]) == bool) or (COR[x]<0) or (COR[x]=='auto'): firstcor, lastcor = 0, int(gdata['nangles'])-1 projs, flat, dark, floc = dxchange.read_als_832h5(inputPath[x]+filename[x]+'.h5', ind_tomo=(firstcor, lastcor)) projs = tomopy.normalize_nf(projs, flat, dark, floc) cor = tomopy.find_center_pc(projs[0], projs[1], tol=0.25) else: cor = COR[x] logging.info('Dataset %s, has %d total slices, reconstructing slices %d through %d in %d substack(s), using COR: %f',filename[x], int(gdata['nslices']), sinorange[0], sinorange[1]-1, substacks, cor) for y in range(0, substacks): logging.info('Starting dataset %s (%d of %d), substack %d of %d',filename[x], x+1, len(filename), y+1, substacks) logging.info('Reading sinograms...') projs, flat, dark, floc = dxchange.read_als_832h5(inputPath[x]+filename[x]+'.h5', sino=(sinorange[0]+y*num_sino_per_substack, sinorange[0]+(y+1)*num_sino_per_substack, 1)) logging.info('Doing remove outliers, norm (nearest flats), and -log...') if doOutliers: projs = tomopy.remove_outlier(projs, outlier_diff, size=outlier_size, axis=0) flat = tomopy.remove_outlier(flat, outlier_diff, size=outlier_size, axis=0) tomo = tomopy.normalize_nf(projs, flat, dark, floc) tomo = tomopy.minus_log(tomo, out=tomo) # in place logarithm # Use padding to remove halo in reconstruction if present if pad_sino: npad = int(np.ceil(tomo.shape[2] * np.sqrt(2)) - tomo.shape[2])//2 tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge') cor_rec = cor + npad # account for padding else: cor_rec = cor if doFWringremoval: logging.info('Doing ring (Fourier-wavelet) function...') tomo = tomopy.remove_stripe_fw(tomo, sigma=ringSigma, level=ringLevel, pad=True, wname=ringWavelet) if doPhaseRetrieval: logging.info('Doing Phase retrieval...') #tomo = tomopy.retrieve_phase(tomo, pixel_size=pxsize, dist=propagation_dist, energy=kev, alpha=alphaReg, pad=True) tomo = tomopy.retrieve_phase(tomo, pixel_size=pxsize, dist=propagation_dist, energy=kev, alpha=alphaReg, pad=True) logging.info('Doing recon (gridrec) function and scaling/masking, with cor %f...',cor_rec) rec = tomopy.recon(tomo, tomopy.angles(tomo.shape[0], 270, 90), center=cor_rec, algorithm='gridrec', filter_name='butterworth', filter_par=butterworthpars) #rec = tomopy.recon(tomo, tomopy.angles(tomo.shape[0], 180+angularrange/2, 180-angularrange/2), center=cor_rec, algorithm='gridrec', filter_name='butterworth', filter_par=butterworthpars) rec /= pxsize # intensity values in cm^-1 if pad_sino: rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0) else: rec = tomopy.circ_mask(rec, 0, ratio=1.0, val=0.0) if doPolarRing: logging.info('Doing ring (polar mean filter) function...') rec = tomopy.remove_ring(rec, theta_min=Rarc, rwidth=Rmaxwidth, thresh_max=Rtmax, thresh=Rthr, thresh_min=Rtmin) logging.info('Writing reconstruction slices to %s', filename[x]) #dxchange.write_tiff_stack(rec, fname=outputPath+'alpha'+str(alphaReg)+'/rec'+filename[x]+'/rec'+filename[x], start=sinorange[0]+y*num_sino_per_substack) dxchange.write_tiff_stack(rec, fname=outputPath + 'recon_'+filename[x]+'/recon_'+filename[x], start=sinorange[0]+y*num_sino_per_substack) logging.info('Reconstruction Complete: '+ filename[x])
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'): sample_detector_distance = 8 # Propagation distance of the wavefront in cm detector_pixel_size_x = 2.247e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4) monochromator_energy = 24.9 # Energy of incident wave in keV alpha = 1e-02 # Phase retrieval coeff. zinger_level = 800 # Zinger level for projections zinger_level_w = 1000 # Zinger level for white # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) # zinger_removal # proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0) # flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0) # Flat-field correction of raw data. ##data = tomopy.normalize(proj, flat, dark, cutoff=0.8) data = tomopy.normalize(proj, flat, dark) # remove stripes data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=2,pad=True) #data = tomopy.remove_stripe_ti(data, alpha=1.5) data = tomopy.remove_stripe_sf(data, size=150) # phase retrieval #data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 rot_center = rot_center/np.power(2, float(binning)) data = tomopy.downsample(data, level=binning) data = tomopy.downsample(data, level=binning, axis=1) # padding N = data.shape[2] data_pad = np.zeros([data.shape[0],data.shape[1],3*N//2],dtype = "float32") data_pad[:,:,N//4:5*N//4] = data data_pad[:,:,0:N//4] = np.tile(np.reshape(data[:,:,0],[data.shape[0],data.shape[1],1]),(1,1,N//4)) data_pad[:,:,5*N//4:] = np.tile(np.reshape(data[:,:,-1],[data.shape[0],data.shape[1],1]),(1,1,N//4)) data = data_pad rot_center = rot_center+N//4 # Reconstruct object. if algorithm == 'sirtfbp': rec = rec_sirtfbp(data, theta, rot_center) else: rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen') rec = rec[:,N//4:5*N//4,N//4:5*N//4] print("Algorithm: ", algorithm) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) return rec
# Set path to the micro-CT data to reconstruct. fname = '../../../tomopy/data/tooth.h5' # Select the sinogram range to reconstruct. start = 0 end = 2 # Read the APS 2-BM 0r 32-ID raw data. proj, flat, dark, theta = dxchange.read_aps_32id(fname, sino=(start, end)) # Set data collection angles as equally spaced between 0-180 degrees. theta = tomopy.angles(proj.shape[0]) # Set data collection angles as equally spaced between 0-180 degrees. proj = tomopy.normalize(proj, flat, dark) # Set data collection angles as equally spaced between 0-180 degrees. rot_center = tomopy.find_center(proj, theta, init=290, ind=0, tol=0.5) proj = tomopy.minus_log(proj) # Reconstruct object using Gridrec algorithm. recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec') # Mask each reconstructed slice with a circle. recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) # Write data as stack of TIFs. dxchange.write_tiff_stack(recon, fname='recon_dir/recon')
logging.info('...with center of rotation shifted %f',k) rec = tomopy.recon(tomo, tomopy.angles(tomo.shape[0], 270, 270-angularrange), center=cor_rec+k, algorithm='gridrec', filter_name='butterworth', filter_par=butterworthpars) rec /= pxsize # intensity values in cm^-1 CORtoWrite =cor_rec+k if doPolarRing: logging.info('Doing ring removal (polar mean filter)') rec = tomopy.remove_ring(rec, theta_min=Rarc, rwidth=Rmaxwidth, thresh_max=Rtmax, thresh=Rthr, thresh_min=Rtmin) if pad_sino: logging.info('Unpadding...') rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0) CORtoWrite = CORtoWrite - npad else: rec = tomopy.circ_mask(rec, 0, ratio=1.0, val=0.0) logging.info('Writing reconstruction slices to %s', iname[x]) if testCOR_insteps: filenametowrite = odirectory+'/rec'+iname[x]+'/'+'cor'+str(CORtoWrite)+'_'+iname[x] else: filenametowrite = odirectory+'/rec'+iname[x]+'/'+iname[x] if castTo8bit: rec = convert8bit(rec,data_min,data_max)
def reconstruct(h5fname, sino, rot_center, args, blocked_views=None): # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) # Manage the missing angles: if blocked_views is not None: print("Blocked Views: ", blocked_views) proj = np.concatenate((proj[0:blocked_views[0], :, :], proj[blocked_views[1]+1:-1, :, :]), axis=0) theta = np.concatenate((theta[0:blocked_views[0]], theta[blocked_views[1]+1: -1])) # Flat-field correction of raw data. data = tomopy.normalize(proj, flat, dark, cutoff=1.4) # remove stripes data = tomopy.remove_stripe_fw(data, level=7, wname='sym16', sigma=1, pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 algorithm = args.algorithm ncores = args.ncores nitr = args.num_iter # always add algorithm _kwargs = {"algorithm": algorithm} # assign number of cores _kwargs["ncore"] = ncores # use the accelerated version if algorithm in ["mlem", "sirt"]: _kwargs["accelerated"] = True # don't assign "num_iter" if gridrec or fbp if algorithm not in ["fbp", "gridrec"]: _kwargs["num_iter"] = nitr sname = os.path.join(args.output_dir, 'proj_{}'.format(args.algorithm)) print(proj.shape) tmp = np.zeros((proj.shape[0], proj.shape[2])) tmp[:,:] = proj[:,0,:] output_image(tmp, sname + "." + args.format) # Reconstruct object. with timemory.util.auto_timer( "[tomopy.recon(algorithm='{}')]".format(algorithm)): print("Starting reconstruction with kwargs={}...".format(_kwargs)) rec = tomopy.recon(data, theta, **_kwargs) print("Completed reconstruction...") # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) obj = np.zeros(rec.shape, dtype=rec.dtype) label = "{} @ {}".format(algorithm.upper(), h5fname) quantify_difference(label, obj, rec) return rec
def _apply_mask(self, recon): ratio = self.parameters['ratio'] if ratio: recon = tomopy.circ_mask(recon, axis=0, ratio=ratio) return self._transpose(recon)
def recon( filename, inputPath = './', outputPath = None, outputFilename = None, doOutliers1D = False, # outlier removal in 1d (along sinogram columns) outlier_diff1D = 750, # difference between good data and outlier data (outlier removal) outlier_size1D = 3, # radius around each pixel to look for outliers (outlier removal) doOutliers2D = False, # outlier removal, standard 2d on each projection outlier_diff2D = 750, # difference between good data and outlier data (outlier removal) outlier_size2D = 3, # radius around each pixel to look for outliers (outlier removal) doFWringremoval = True, # Fourier-wavelet ring removal doTIringremoval = False, # Titarenko ring removal doSFringremoval = False, # Smoothing filter ring removal ringSigma = 3, # damping parameter in Fourier space (Fourier-wavelet ring removal) ringLevel = 8, # number of wavelet transform levels (Fourier-wavelet ring removal) ringWavelet = 'db5', # type of wavelet filter (Fourier-wavelet ring removal) ringNBlock = 0, # used in Titarenko ring removal (doTIringremoval) ringAlpha = 1.5, # used in Titarenko ring removal (doTIringremoval) ringSize = 5, # used in smoothing filter ring removal (doSFringremoval) doPhaseRetrieval = False, # phase retrieval alphaReg = 0.0002, # smaller = smoother (used for phase retrieval) propagation_dist = 75, # sample-to-scintillator distance (phase retrieval) kev = 24, # energy level (phase retrieval) butterworth_cutoff = 0.25, #0.1 would be very smooth, 0.4 would be very grainy (reconstruction) butterworth_order = 2, # for reconstruction doTranslationCorrection = False, # correct for linear drift during scan xshift = 0, # undesired dx transation correction (from 0 degree to 180 degree proj) yshift = 0, # undesired dy transation correction (from 0 degree to 180 degree proj) doPolarRing = False, # ring removal Rarc=30, # min angle needed to be considered ring artifact (ring removal) Rmaxwidth=100, # max width of rings to be filtered (ring removal) Rtmax=3000.0, # max portion of image to filter (ring removal) Rthr=3000.0, # max value of offset due to ring artifact (ring removal) Rtmin=-3000.0, # min value of image to filter (ring removal) cor=None, # center of rotation (float). If not used then cor will be detected automatically corFunction = 'pc', # center of rotation function to use - can be 'pc', 'vo', or 'nm' voInd = None, # index of slice to use for cor search (vo) voSMin = -40, # min radius for searching in sinogram (vo) voSMax = 40, # max radius for searching in sinogram (vo) voSRad = 10, # search radius (vo) voStep = 0.5, # search step (vo) voRatio = 2.0, # ratio of field-of-view and object size (vo) voDrop = 20, # drop lines around vertical center of mask (vo) nmInd = None, # index of slice to use for cor search (nm) nmInit = None, # initial guess for center (nm) nmTol = 0.5, # desired sub-pixel accuracy (nm) nmMask = True, # if True, limits analysis to circular region (nm) nmRatio = 1.0, # ratio of radius of circular mask to edge of reconstructed image (nm) nmSinoOrder = False, # if True, analyzes in sinogram space. If False, analyzes in radiograph space use360to180 = False, # use 360 to 180 conversion doBilateralFilter = False, # if True, uses bilateral filter on image just before write step # NOTE: image will be converted to 8bit if it is not already bilateral_srad = 3, # spatial radius for bilateral filter (image will be converted to 8bit if not already) bilateral_rrad = 30, # range radius for bilateral filter (image will be converted to 8bit if not already) castTo8bit = False, # convert data to 8bit before writing cast8bit_min=-10, # min value if converting to 8bit cast8bit_max=30, # max value if converting to 8bit useNormalize_nf = False, # normalize based on background intensity (nf) chunk_proj = 100, # chunk size in projection direction chunk_sino = 100, # chunk size in sinogram direction npad = None, # amount to pad data before reconstruction projused = None, #should be slicing in projection dimension (start,end,step) sinoused = None, #should be sliceing in sinogram dimension (start,end,step). If first value is negative, it takes the number of slices from the second value in the middle of the stack. correcttilt = 0, #tilt dataset tiltcenter_slice = None, # tilt center (x direction) tiltcenter_det = None, # tilt center (y direction) angle_offset = 0, #this is the angle offset from our default (270) so that tomopy yields output in the same orientation as previous software (Octopus) anglelist = None, #if not set, will assume evenly spaced angles which will be calculated by the angular range and number of angles found in the file. if set to -1, will read individual angles from each image. alternatively, a list of angles can be passed. doBeamHardening = False, #turn on beam hardening correction, based on "Correction for beam hardening in computed tomography", Gabor Herman, 1979 Phys. Med. Biol. 24 81 BeamHardeningCoefficients = None, #6 values, tomo = a0 + a1*tomo + a2*tomo^2 + a3*tomo^3 + a4*tomo^4 + a5*tomo^5 projIgnoreList = None, #projections to be ignored in the reconstruction (for simplicity in the code, they will not be removed and will be processed as all other projections but will be set to zero absorption right before reconstruction. *args, **kwargs): start_time = time.time() print("Start {} at:".format(filename)+time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime())) outputPath = inputPath if outputPath is None else outputPath outputFilename = filename if outputFilename is None else outputFilename outputFilename = outputFilename.replace('.h5','') tempfilenames = [outputPath+'tmp0.h5',outputPath+'tmp1.h5'] filenametowrite = outputPath+'/rec'+filename.strip(".h5")+'/'+outputFilename #filenametowrite = outputPath+'/rec'+filename+'/'+outputFilename print("cleaning up previous temp files", end="") for tmpfile in tempfilenames: try: os.remove(tmpfile) except OSError: pass print(", reading metadata") datafile = h5py.File(inputPath+filename, 'r') gdata = dict(dxchange.reader._find_dataset_group(datafile).attrs) pxsize = float(gdata['pxsize'])/10 # /10 to convert units from mm to cm numslices = int(gdata['nslices']) numangles = int(gdata['nangles']) angularrange = float(gdata['arange']) numrays = int(gdata['nrays']) npad = int(np.ceil(numrays * np.sqrt(2)) - numrays)//2 if npad is None else npad projused = (0,numangles-1,1) if projused is None else projused # ndark = int(gdata['num_dark_fields']) # ind_dark = list(range(0, ndark)) # group_dark = [numangles - 1] inter_bright = int(gdata['i0cycle']) nflat = int(gdata['num_bright_field']) ind_flat = list(range(0, nflat)) if inter_bright > 0: group_flat = list(range(0, numangles, inter_bright)) if group_flat[-1] != numangles - 1: group_flat.append(numangles - 1) elif inter_bright == 0: group_flat = [0, numangles - 1] else: group_flat = None ind_tomo = list(range(0, numangles)) floc_independent = dxchange.reader._map_loc(ind_tomo, group_flat) #figure out the angle list (a list of angles, one per projection image) dtemp = datafile[list(datafile.keys())[0]] fltemp = list(dtemp.keys()) firstangle = float(dtemp[fltemp[0]].attrs.get('rot_angle',0)) if anglelist is None: #the offset angle should offset from the angle of the first image, which is usually 0, but in the case of timbir data may not be. #we add the 270 to be inte same orientation as previous software used at bl832 angle_offset = 270 + angle_offset - firstangle anglelist = tomopy.angles(numangles, angle_offset, angle_offset-angularrange) elif anglelist==-1: anglelist = np.zeros(shape=numangles) for icount in range(0,numangles): anglelist[icount] = np.pi/180*(270 + angle_offset - float(dtemp[fltemp[icount]].attrs['rot_angle'])) #if projused is different than default, need to chnage numangles and angularrange #can't do useNormalize_nf and doOutliers2D at the same time, or doOutliers2D and doOutliers1D at the same time, b/c of the way we chunk, for now just disable that if useNormalize_nf==True and doOutliers2D==True: useNormalize_nf = False print("we cannot currently do useNormalize_nf and doOutliers2D at the same time, turning off useNormalize_nf") if doOutliers2D==True and doOutliers1D==True: doOutliers1D = False print("we cannot currently do doOutliers1D and doOutliers2D at the same time, turning off doOutliers1D") #figure out how user can pass to do central x number of slices, or set of slices dispersed throughout (without knowing a priori the value of numslices) if sinoused is None: sinoused = (0,numslices,1) elif sinoused[0]<0: sinoused=(int(np.floor(numslices/2.0)-np.ceil(sinoused[1]/2.0)),int(np.floor(numslices/2.0)+np.floor(sinoused[1]/2.0)),1) num_proj_per_chunk = np.minimum(chunk_proj,projused[1]-projused[0]) numprojchunks = (projused[1]-projused[0]-1)//num_proj_per_chunk+1 num_sino_per_chunk = np.minimum(chunk_sino,sinoused[1]-sinoused[0]) numsinochunks = (sinoused[1]-sinoused[0]-1)//num_sino_per_chunk+1 numprojused = (projused[1]-projused[0])//projused[2] numsinoused = (sinoused[1]-sinoused[0])//sinoused[2] BeamHardeningCoefficients = (0, 1, 0, 0, 0, .1) if BeamHardeningCoefficients is None else BeamHardeningCoefficients if cor is None: print("Detecting center of rotation", end="") if angularrange>300: lastcor = int(np.floor(numangles/2)-1) else: lastcor = numangles-1 #I don't want to see the warnings about the reader using a deprecated variable in dxchange with warnings.catch_warnings(): warnings.simplefilter("ignore") tomo, flat, dark, floc = dxchange.read_als_832h5(inputPath+filename,ind_tomo=(0,lastcor)) tomo = tomo.astype(np.float32) if useNormalize_nf: tomopy.normalize_nf(tomo, flat, dark, floc, out=tomo) else: tomopy.normalize(tomo, flat, dark, out=tomo) if corFunction == 'vo': # same reason for catching warnings as above with warnings.catch_warnings(): warnings.simplefilter("ignore") cor = tomopy.find_center_vo(tomo, ind=voInd, smin=voSMin, smax=voSMax, srad=voSRad, step=voStep, ratio=voRatio, drop=voDrop) elif corFunction == 'nm': cor = tomopy.find_center(tomo, tomopy.angles(numangles, angle_offset, angle_offset-angularrange), ind=nmInd, init=nmInit, tol=nmTol, mask=nmMask, ratio=nmRatio, sinogram_order=nmSinoOrder) elif corFunction == 'pc': cor = tomopy.find_center_pc(tomo[0], tomo[1], tol=0.25) else: raise ValueError("\'corFunction\' must be one of: [ pc, vo, nm ].") print(", {}".format(cor)) else: print("using user input center of {}".format(cor)) function_list = [] if doOutliers1D: function_list.append('remove_outlier1d') if doOutliers2D: function_list.append('remove_outlier2d') if useNormalize_nf: function_list.append('normalize_nf') else: function_list.append('normalize') function_list.append('minus_log') if doBeamHardening: function_list.append('beam_hardening') if doFWringremoval: function_list.append('remove_stripe_fw') if doTIringremoval: function_list.append('remove_stripe_ti') if doSFringremoval: function_list.append('remove_stripe_sf') if correcttilt: function_list.append('correcttilt') if use360to180: function_list.append('do_360_to_180') if doPhaseRetrieval: function_list.append('phase_retrieval') function_list.append('recon_mask') if doPolarRing: function_list.append('polar_ring') if castTo8bit: function_list.append('castTo8bit') if doBilateralFilter: function_list.append('bilateral_filter') function_list.append('write_output') # Figure out first direction to slice for func in function_list: if slice_dir[func] != 'both': axis = slice_dir[func] break done = False curfunc = 0 curtemp = 0 while True: # Loop over reading data in certain chunking direction if axis=='proj': niter = numprojchunks else: niter = numsinochunks for y in range(niter): # Loop over chunks print("{} chunk {} of {}".format(axis, y+1, niter)) if curfunc==0: with warnings.catch_warnings(): warnings.simplefilter("ignore") if axis=='proj': tomo, flat, dark, floc = dxchange.read_als_832h5(inputPath+filename,ind_tomo=range(y*num_proj_per_chunk+projused[0],np.minimum((y + 1)*num_proj_per_chunk+projused[0],numangles)),sino=(sinoused[0],sinoused[1], sinoused[2]) ) else: tomo, flat, dark, floc = dxchange.read_als_832h5(inputPath+filename,ind_tomo=range(projused[0],projused[1],projused[2]),sino=(y*num_sino_per_chunk+sinoused[0],np.minimum((y + 1)*num_sino_per_chunk+sinoused[0],numslices),1) ) else: if axis=='proj': start, end = y * num_proj_per_chunk, np.minimum((y + 1) * num_proj_per_chunk,numprojused) tomo = dxchange.reader.read_hdf5(tempfilenames[curtemp],'/tmp/tmp',slc=((start,end,1),(0,numslices,1),(0,numrays,1))) #read in intermediate file else: start, end = y * num_sino_per_chunk, np.minimum((y + 1) * num_sino_per_chunk,numsinoused) tomo = dxchange.reader.read_hdf5(tempfilenames[curtemp],'/tmp/tmp',slc=((0,numangles,1),(start,end,1),(0,numrays,1))) dofunc = curfunc keepvalues = None while True: # Loop over operations to do in current chunking direction func_name = function_list[dofunc] newaxis = slice_dir[func_name] if newaxis != 'both' and newaxis != axis: # We have to switch axis, so flush to disk if y==0: try: os.remove(tempfilenames[1-curtemp]) except OSError: pass appendaxis = 1 if axis=='sino' else 0 dxchange.writer.write_hdf5(tomo,fname=tempfilenames[1-curtemp],gname='tmp',dname='tmp',overwrite=False,appendaxis=appendaxis) #writing intermediate file... break print(func_name, end=" ") curtime = time.time() if func_name == 'remove_outlier1d': tomo = tomo.astype(np.float32,copy=False) remove_outlier1d(tomo, outlier_diff1D, size=outlier_size1D, out=tomo) if func_name == 'remove_outlier2d': tomo = tomo.astype(np.float32,copy=False) tomopy.remove_outlier(tomo, outlier_diff2D, size=outlier_size2D, axis=0, out=tomo) elif func_name == 'normalize_nf': tomo = tomo.astype(np.float32,copy=False) tomopy.normalize_nf(tomo, flat, dark, floc_independent, out=tomo) #use floc_independent b/c when you read file in proj chunks, you don't get the correct floc returned right now to use here. elif func_name == 'normalize': tomo = tomo.astype(np.float32,copy=False) tomopy.normalize(tomo, flat, dark, out=tomo) elif func_name == 'minus_log': mx = np.float32(0.00000000000000000001) ne.evaluate('where(tomo>mx, tomo, mx)', out=tomo) tomopy.minus_log(tomo, out=tomo) elif func_name == 'beam_hardening': loc_dict = {'a{}'.format(i):np.float32(val) for i,val in enumerate(BeamHardeningCoefficients)} tomo = ne.evaluate('a0 + a1*tomo + a2*tomo**2 + a3*tomo**3 + a4*tomo**4 + a5*tomo**5', local_dict=loc_dict, out=tomo) elif func_name == 'remove_stripe_fw': tomo = tomopy.remove_stripe_fw(tomo, sigma=ringSigma, level=ringLevel, pad=True, wname=ringWavelet) elif func_name == 'remove_stripe_ti': tomo = tomopy.remove_stripe_ti(tomo, nblock=ringNBlock, alpha=ringAlpha) elif func_name == 'remove_stripe_sf': tomo = tomopy.remove_stripe_sf(tomo, size=ringSize) elif func_name == 'correcttilt': if tiltcenter_slice is None: tiltcenter_slice = numslices/2. if tiltcenter_det is None: tiltcenter_det = tomo.shape[2]/2 new_center = tiltcenter_slice - 0.5 - sinoused[0] center_det = tiltcenter_det - 0.5 #add padding of 10 pixels, to be unpadded right after tilt correction. This makes the tilted image not have zeros at certain edges, which matters in cases where sample is bigger than the field of view. For the small amounts we are generally tilting the images, 10 pixels is sufficient. # tomo = tomopy.pad(tomo, 2, npad=10, mode='edge') # center_det = center_det + 10 cntr = (center_det, new_center) for b in range(tomo.shape[0]): tomo[b] = st.rotate(tomo[b], correcttilt, center=cntr, preserve_range=True, order=1, mode='edge', clip=True) #center=None means image is rotated around its center; order=1 is default, order of spline interpolation # tomo = tomo[:, :, 10:-10] elif func_name == 'do_360_to_180': # Keep values around for processing the next chunk in the list keepvalues = [angularrange, numangles, projused, num_proj_per_chunk, numprojchunks, numprojused, numrays, anglelist] #why -.5 on one and not on the other? if tomo.shape[0]%2>0: tomo = sino_360_to_180(tomo[0:-1,:,:], overlap=int(np.round((tomo.shape[2]-cor-.5))*2), rotation='right') angularrange = angularrange/2 - angularrange/(tomo.shape[0]-1) else: tomo = sino_360_to_180(tomo[:,:,:], overlap=int(np.round((tomo.shape[2]-cor))*2), rotation='right') angularrange = angularrange/2 numangles = int(numangles/2) projused = (0,numangles-1,1) num_proj_per_chunk = np.minimum(chunk_proj,projused[1]-projused[0]) numprojchunks = (projused[1]-projused[0]-1)//num_proj_per_chunk+1 numprojused = (projused[1]-projused[0])//projused[2] numrays = tomo.shape[2] anglelist = anglelist[:numangles] elif func_name == 'phase_retrieval': tomo = tomopy.retrieve_phase(tomo, pixel_size=pxsize, dist=propagation_dist, energy=kev, alpha=alphaReg, pad=True) elif func_name == 'translation_correction': tomo = linear_translation_correction(tomo,dx=xshift,dy=yshift,interpolation=False): elif func_name == 'recon_mask': tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge') if projIgnoreList is not None: for badproj in projIgnoreList: tomo[badproj] = 0 rec = tomopy.recon(tomo, anglelist, center=cor+npad, algorithm='gridrec', filter_name='butterworth', filter_par=[butterworth_cutoff, butterworth_order]) rec = rec[:, npad:-npad, npad:-npad] rec /= pxsize # convert reconstructed voxel values from 1/pixel to 1/cm rec = tomopy.circ_mask(rec, 0) elif func_name == 'polar_ring': rec = np.ascontiguousarray(rec, dtype=np.float32) rec = tomopy.remove_ring(rec, theta_min=Rarc, rwidth=Rmaxwidth, thresh_max=Rtmax, thresh=Rthr, thresh_min=Rtmin,out=rec) elif func_name == 'castTo8bit': rec = convert8bit(rec, cast8bit_min, cast8bit_max) elif func_name == 'bilateral_filter': rec = pyF3D.run_BilateralFilter(rec, spatialRadius=bilateral_srad, rangeRadius=bilateral_rrad) elif func_name == 'write_output': dxchange.write_tiff_stack(rec, fname=filenametowrite, start=y*num_sino_per_chunk + sinoused[0]) print('(took {:.2f} seconds)'.format(time.time()-curtime)) dofunc+=1 if dofunc==len(function_list): break if y<niter-1 and keepvalues: # Reset original values for next chunk angularrange, numangles, projused, num_proj_per_chunk, numprojchunks, numprojused, numrays, anglelist = keepvalues curtemp = 1 - curtemp curfunc = dofunc if curfunc==len(function_list): break axis = slice_dir[function_list[curfunc]] print("cleaning up temp files") for tmpfile in tempfilenames: try: os.remove(tmpfile) except OSError: pass print("End Time: "+time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime())) print('It took {:.3f} s to process {}'.format(time.time()-start_time,inputPath+filename))
def main(arg): parser = argparse.ArgumentParser() parser.add_argument("fname", help="Full file name: /data/fname.raw") parser.add_argument("--start", nargs='?', type=int, default=0, help="First image to read") parser.add_argument("--nimg", nargs='?', type=int, default=1, help="Number of images to read") parser.add_argument("--ndark", nargs='?', type=int, default=10, help="Number of dark images") parser.add_argument("--nflat", nargs='?', type=int, default=10, help="Number of white images") args = parser.parse_args() fname = args.fname start = args.start end = args.start + args.nimg nflat, ndark, nimg, height, width = read_adimec_header(fname) print("Image Size:", width, height) print("Dataset metadata (nflat, ndark, nimg:", nflat, ndark, nimg) # override nflat and ndark from header with the passed parameter # comment the two lines below if the meta data in the binary # file for nflat and ndark is correct nflat = args.nflat ndark = args.ndark proj = read_adimec_stack(fname, img=(start, end)) print("Projection:", proj.shape) # slider(proj) flat = read_adimec_stack(fname, img=(nimg-ndark-nflat, nimg-ndark)) print("Flat:", flat.shape) # slider(flat) dark = read_adimec_stack(fname, img=(nimg-ndark, nimg)) print("Dark:", dark.shape) # slider(dark) nproj = tomopy.normalize(proj, flat, dark) print("Normalized projection:", nproj.shape) # slider(proj) proj = nproj[:,100:110, :] print("Sino chunk:", proj.shape) slider(proj) theta = tomopy.angles(proj.shape[0]) print(theta.shape) proj = tomopy.minus_log(proj) proj = tomopy.remove_nan(proj, val=0.0) proj = tomopy.remove_neg(proj, val=0.00) proj[np.where(proj == np.inf)] = 0.00 rot_center = 1280 # Reconstruct object using Gridrec algorithm. rec = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec') # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) # Write data as stack of TIFs. dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def fast_tomo_recon(argv): """ Reconstruct subset slices (sinograms) equally spaced within tomographic dataset """ logger = logging.getLogger("fast_tomopy.fast_tomo_recon") # Parse arguments passed to function parser = argparse.ArgumentParser() parser.add_argument("-i", "--input", type=str, help="path to input raw " "dataset", required=True) parser.add_argument( "-o", "--output-file", type=str, help="full path to h5 output " "file", default=os.path.join(os.getcwd(), "fast-tomopy.h5"), ) parser.add_argument("-sn", "--sino-num", type=int, help="Number of slices " "to reconstruct", default=5) parser.add_argument( "-a", "--algorithm", type=str, help="Reconstruction" " algorithm", default="gridrec", choices=[ "art", "bart", "fbp", "gridrec", "mlem", "ospml_hybrid", "ospml_quad", "pml_hybrid", "pml_quad", "sirt", ], ) parser.add_argument("-c", "--center", type=float, help="Center of rotation", default=None) parser.add_argument( "-fn", "--filter-name", type=str, help="Name of filter" " used for reconstruction", choices=["none", "shepp", "cosine", "hann", "hamming", "ramlak", "parzen", "butterworth"], default="butterworth", ) parser.add_argument( "-rr", "--ring-remove", type=str, help="Ring removal " "method", choices=["Octopus", "Tomopy-FW", "Tomopy-T"], default="Tomopy-T", ) parser.add_argument("-lf", "--log-file", type=str, help="log file name", default="fast-tomopy.log") args = parser.parse_args() fh = logging.FileHandler(args.log_file) fh.setLevel(logging.INFO) fh.setFormatter(formatter) logger.addHandler(fh) if os.path.isdir(os.path.dirname(args.output_file)) is False: raise IOError(2, "Directory of output file does not exist", args.output_file) # Read file metadata logger.info("Reading input file metadata") fdata, gdata = read_als_832h5_metadata(args.input) proj_total = int(gdata["nangles"]) last = proj_total - 1 sino_total = int(gdata["nslices"]) ray_total = int(gdata["nrays"]) px_size = float(gdata["pxsize"]) / 10 # cm # Set parameters for sinograms to read step = sino_total // (args.sino_num + 2) start = step end = step * (args.sino_num + 1) sino = (start, end, step) # Read full first and last projection to determine center of rotation if args.center is None: logger.info("Reading full first and last projection for COR") first_last, flats, darks, floc = tomopy.read_als_832h5(args.input, ind_tomo=(0, last)) first_last = tomopy.normalize(first_last, flats, darks) args.center = tomopy.find_center_pc(first_last[0, :, :], first_last[1, :, :], tol=0.1) logger.info("Detected center: %f", args.center) # Read and normalize raw sinograms logger.info("Reading raw data") tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino) logger.info("Normalizing raw data") tomo = tomopy.normalize_nf(tomo, flats, darks, floc) # Remove stripes from sinograms (remove rings) logger.info("Preprocessing normalized data") if args.ring_remove == "Tomopy-FW": logger.info("Removing stripes from sinograms with %s", args.ring_remove) tomo = tomopy.remove_stripe_fw(tomo) elif args.ring_remove == "Tomopy-T": logger.info("Removing stripes from sinograms with %s", args.ring_remove) tomo = tomopy.remove_stripe_ti(tomo) # Pad sinograms with edge values npad = int(np.ceil(ray_total * np.sqrt(2)) - ray_total) // 2 tomo = tomopy.pad(tomo, 2, npad=npad, mode="edge") args.center += npad # account for padding filter_name = np.array(args.filter_name, dtype=(str, 16)) theta = tomopy.angles(proj_total, 270, 90) logger.info("Reconstructing normalized data") # Reconstruct sinograms # rec = tomopy.minus_log(tomo, out=tomo) rec = tomopy.recon( tomo, theta, center=args.center, emission=False, algorithm=args.algorithm, filter_name=filter_name ) rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0) rec = rec / px_size # Remove rings from reconstruction if args.ring_remove == "Octopus": logger.info("Removing rings from reconstructions with %s", args.ring_remove) thresh = float(gdata["ring_threshold"]) thresh_max = float(gdata["upp_ring_value"]) thresh_min = float(gdata["low_ring_value"]) theta_min = int(gdata["max_arc_length"]) rwidth = int(gdata["max_ring_size"]) rec = tomopy.remove_rings( rec, center_x=args.center, thresh=thresh, thresh_max=thresh_max, thresh_min=thresh_min, theta_min=theta_min, rwidth=rwidth, ) # Write reconstruction data to new hdf5 file fdata["stage"] = "fast-tomopy" fdata["stage_flow"] = "/raw/" + fdata["stage"] fdata["stage_version"] = "fast-tomopy-0.1" # Generate a new uuid based on host ID and current time fdata["uuid"] = str(uuid.uuid1()) gdata["Reconstruction_Type"] = "tomopy-gridrec" gdata["ring_removal_method"] = args.ring_remove gdata["rfilter"] = args.filter_name logger.info("Writing reconstructed data to h5 file") write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step) return
def rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning): data_shape = get_dx_dims(h5fname, 'data') print(data_shape) ssino = int(data_shape[1] * nsino) rot_center+=data_shape[2]//4 center_range = (rot_center-center_search_width, rot_center+center_search_width, 0.5) #print(sino,ssino, center_range) #print(center_range[0], center_range[1], center_range[2]) # Select sinogram range to reconstruct sino = None start = ssino end = start + 1 sino = (start, end) # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) # Flat-field correction of raw data. data = tomopy.normalize(proj, flat, dark, cutoff=1.4) data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=2,pad=True) #data = tomopy.remove_stripe_ti(data, alpha=1.5) data = tomopy.remove_stripe_sf(data, size=150) # remove stripes # data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) # padding N = data.shape[2] data_pad = np.zeros([data.shape[0],data.shape[1],3*N//2],dtype = "float32") data_pad[:,:,N//4:5*N//4] = data data_pad[:,:,0:N//4] = np.tile(np.reshape(data[:,:,0],[data.shape[0],data.shape[1],1]),(1,1,N//4)) data_pad[:,:,5*N//4:] = np.tile(np.reshape(data[:,:,-1],[data.shape[0],data.shape[1],1]),(1,1,N//4)) data = data_pad stack = np.empty((len(np.arange(*center_range)), data.shape[0], data.shape[2])) print(stack.shape) print(data.shape) index = 0 for axis in np.arange(*center_range): stack[index] = data[:, 0, :] index = index + 1 # Reconstruct the same slice with a range of centers. rec = tomopy.recon(stack, theta, center=np.arange(*center_range), sinogram_order=True, algorithm='gridrec', filter_name='parzen', nchunk=1) rec = rec[:,N//4:5*N//4,N//4:5*N//4] # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) index = 0 # Save images to a temporary folder. fname = os.path.dirname(h5fname) + '/' + 'try_rec/' + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0] for axis in np.arange(*center_range): rfname = fname + '_' + str('{0:.2f}'.format(axis-N//4) + '.tiff') dxchange.write_tiff(rec[index], fname=rfname, overwrite=True) index = index + 1 print("Reconstructions: ", fname)
def recon_block(grid, shift_grid, src_folder, dest_folder, slice_range, sino_step, center_vec, ds_level=0, blend_method='max', blend_options=None, tolerance=1, sinogram_order=False, algorithm='gridrec', init_recon=None, ncore=None, nchunk=None, dtype='float32', crop=None, save_sino=False, assert_width=None, sino_blur=None, color_correction=False, flattened_radius=120, normalize=True, test_mode=False, mode='180', phase_retrieval=None, **kwargs): """ Reconstruct dsicrete HDF5 tiles, blending sinograms only. """ raw_folder = os.getcwd() os.chdir(src_folder) sino_ini = int(slice_range[0]) sino_end = int(slice_range[1]) mod_start_slice = 0 center_vec = np.asarray(center_vec) center_pos_cache = 0 sino_ls = np.arange(sino_ini, sino_end, sino_step, dtype='int') pix_shift_grid = np.ceil(shift_grid) pix_shift_grid[pix_shift_grid < 0] = 0 alloc_set = allocate_mpi_subsets(sino_ls.size, size, task_list=sino_ls) for i_slice in alloc_set[rank]: print('############################################') print('Reconstructing ' + str(i_slice)) # judge from which tile to retrieve sinos grid_lines = np.zeros(grid.shape[1], dtype=np.int) slice_in_tile = np.zeros(grid.shape[1], dtype=np.int) for col in range(grid.shape[1]): bins = pix_shift_grid[:, col, 0] grid_lines[col] = int(np.squeeze(np.digitize(i_slice, bins)) - 1) if grid_lines[col] == -1: print("WARNING: The specified starting slice number does not allow for full sinogram construction. Trying next slice...") mod_start_slice = 1 break else: mod_start_slice = 0 slice_in_tile[col] = i_slice - bins[grid_lines[col]] if mod_start_slice == 1: continue center_pos = int(np.round(center_vec[grid_lines].mean())) if center_pos_cache == 0: center_pos_cache = center_pos center_diff = center_pos - center_pos_cache center_pos_0 = center_pos row_sino, center_pos = prepare_slice(grid, shift_grid, grid_lines, slice_in_tile, ds_level=ds_level, method=blend_method, blend_options=blend_options, rot_center=center_pos, assert_width=assert_width, sino_blur=sino_blur, color_correction=color_correction, normalize=normalize, mode=mode, phase_retrieval=phase_retrieval) rec0 = recon_slice(row_sino, center_pos, sinogram_order=sinogram_order, algorithm=algorithm, init_recon=init_recon, ncore=ncore, nchunk=nchunk, **kwargs) rec = tomopy.remove_ring(np.copy(rec0)) cent = int((rec.shape[1] - 1) / 2) xx, yy = np.meshgrid(np.arange(rec.shape[2]), np.arange(rec.shape[1])) mask0 = ((xx - cent) ** 2 + (yy - cent) ** 2 <= flattened_radius ** 2) mask = np.zeros(rec.shape, dtype='bool') for i in range(mask.shape[0]): mask[i, :, :] = mask0 rec[mask] = (rec[mask] + rec0[mask]) / 2 rec = tomopy.remove_outlier(rec, tolerance) rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) print('Center: {:d}'.format(center_pos)) rec = np.squeeze(rec) if center_diff != 0: rec = np.roll(rec, -center_diff, axis=0) if not crop is None: crop = np.asarray(crop) rec = rec[crop[0, 0]:crop[1, 0], crop[0, 1]:crop[1, 1]] os.chdir(raw_folder) if test_mode: dxchange.write_tiff(rec, fname=os.path.join(dest_folder, 'recon/recon_{:05d}_{:04d}.tiff'.format(i_slice, center_pos)), dtype=dtype) else: dxchange.write_tiff(rec, fname=os.path.join(dest_folder, 'recon/recon_{:05d}.tiff'.format(i_slice)), dtype=dtype) if save_sino: dxchange.write_tiff(np.squeeze(row_sino), fname=os.path.join(dest_folder, 'sino/sino_{:05d}.tiff'.format(i_slice)), overwrite=True) os.chdir(src_folder) os.chdir(raw_folder) return
if remove_stripe2: data = tomopy.remove_stripe_ti(data) #d.downsample2d(level=level) # apply binning on the data data = tomopy.downsample(data, level=level) # apply binning on the data theta = tomopy.angles(data.shape[0]) if 1: #if not best_center: d.optimize_center() if not best_center: calc_center = tomopy.find_center(data, theta, emission=False, ind=0, tol=0.3) else: #d.center=best_center/pow(2,level) # Manage the rotation center calc_center = best_center/pow(2,level) # Manage the rotation center #d.gridrec(ringWidth=RingW) # Run the reconstruction rec = tomopy.recon(data, theta, center=calc_center, algorithm='gridrec', emission=False) #d.apply_mask(ratio=1) rec = tomopy.circ_mask(rec, axis=0) # Write data as stack of TIFs. #tomopy.xtomo_writer(d.data_recon, output_name, # axis=0, # x_start=slice_first) tomopy.io.writer.write_tiff_stack(rec, fname=output_name, axis=0, start=slice_first) #### for the whole volume reconstruction if 1: f = h5py.File(file_name, "r"); nProj, nslices, nCol = f["/exchange3/data"].shape nslices_per_chunk = nslices/chunk for iChunk in range(0,chunk): print '\n -- chunk # %i' % (iChunk+1) slice_first = nslices_per_chunk*iChunk
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'): sample_detector_distance = 25 # Propagation distance of the wavefront in cm detector_pixel_size_x = 2.143e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4) #monochromator_energy = 24.9 # Energy of incident wave in keV # used pink beam alpha = 1e-02 # Phase retrieval coeff. zinger_level = 800 # Zinger level for projections zinger_level_w = 1000 # Zinger level for white # Read APS 2-BM raw data. # DIMAX saves 3 files: proj, flat, dark # when loading the data set select the proj file (larger size) fname = os.path.splitext(h5fname)[0] fbase = fname.rsplit('_', 1)[0] fnum = fname.rsplit('_', 1)[1] fext = os.path.splitext(h5fname)[1] fnum_flat = str("%4.4d" % (int(fnum)+1)) fnum_dark = str("%4.4d" % (int(fnum)+2)) fnproj = fbase + '_' + fnum + fext fnflat = fbase + '_' + fnum_flat + fext fndark = fbase + '_' + fnum_dark + fext fnflat = '/local/data/2018-11/Chawla/1G_A/1G_A_0002.hdf' fndark = '/local/data/2018-11/Chawla/1G_A/1G_A_0003.hdf' print('proj', fnproj) print('flat', fnflat) print('dark', fndark) # Read APS 2-BM DIMAX raw data. proj, dum, dum2, theta = dxchange.read_aps_32id(fnproj, sino=sino) dum3, flat, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino) dum6, dum7, dark, dum8 = dxchange.read_aps_32id(fndark, sino=sino) # Flat-field correction of raw data. data = tomopy.normalize(proj, flat, dark, cutoff=1.4) # remove stripes data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) # zinger_removal proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0) flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0) # Flat-field correction of raw data. ##data = tomopy.normalize(proj, flat, dark, cutoff=0.8) data = tomopy.normalize(proj, flat, dark) # remove stripes #data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) #data = tomopy.remove_stripe_ti(data, alpha=1.5) data = tomopy.remove_stripe_sf(data, size=150) # phase retrieval #data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 rot_center = rot_center/np.power(2, float(binning)) data = tomopy.downsample(data, level=binning) data = tomopy.downsample(data, level=binning, axis=1) # Reconstruct object. if algorithm == 'sirtfbp': rec = rec_sirtfbp(data, theta, rot_center) else: rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen') print("Algorithm: ", algorithm) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) #rec = np.swapaxes(rec,0,2) return rec
# Filters are saved in .mat files in "./¨ test_sirtfbp_iter = True if test_sirtfbp_iter: nCol = data.shape[2] output_name = './test_iter/' num_iter = [50,100,150] filter_dict = sirtfilter.getfilter(nCol, theta, num_iter, filter_dir='./') for its in num_iter: tomopy_filter = sirtfilter.convert_to_tomopy_filter(filter_dict[its], nCol) rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', filter_name='custom2d', filter_par=tomopy_filter) output_name_2 = output_name + 'sirt_fbp_%iiter_slice_' % its dxchange.write_tiff_stack(data, fname=output_name_2, start=start, dtype='float32') # Reconstruct object using sirt-fbp algorithm: num_iter = 100 nCol = data.shape[2] sirtfbp_filter = sirtfilter.getfilter(nCol, theta, num_iter, filter_dir='./') tomopy_filter = sirtfilter.convert_to_tomopy_filter(sirtfbp_filter, nCol) rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', filter_name='custom2d', filter_par=tomopy_filter) # Reconstruct object using Gridrec algorithm. # rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec', nchunk=1) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) # Write data as stack of TIFs. fname='/replace_this_with_data_dir_path/sirtfbp_recon' dxchange.write_tiff_stack(rec, fname=fname)
def rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning, dark_file): data_shape = get_dx_dims(h5fname, 'data') print(data_shape) ssino = int(data_shape[1] * nsino) center_range = (rot_center-center_search_width, rot_center+center_search_width, 0.5) #print(sino,ssino, center_range) #print(center_range[0], center_range[1], center_range[2]) # Select sinogram range to reconstruct sino = None start = ssino end = start + 1 sino = (start, end) # Read APS 32-BM raw data. proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino) if dark_file is not None: print('Reading white/dark from {}'.format(dark_file)) proj_, flat, dark, theta_ = dxchange.read_aps_32id(dark_file, sino=sino) del proj_, theta_ print(proj.shape, flat.shape, dark.shape) # Flat-field correction of raw data. data = tomopy.normalize(proj, flat, dark, cutoff=1.4) # remove stripes # data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True) print("Raw data: ", h5fname) print("Center: ", rot_center) data = tomopy.minus_log(data) data = tomopy.remove_nan(data, val=0.0) data = tomopy.remove_neg(data, val=0.00) data[np.where(data == np.inf)] = 0.00 stack = np.empty((len(np.arange(*center_range)), data_shape[0], data_shape[2])) index = 0 for axis in np.arange(*center_range): stack[index] = data[:, 0, :] index = index + 1 # Reconstruct the same slice with a range of centers. rec = tomopy.recon(stack, theta, center=np.arange(*center_range), sinogram_order=True, algorithm='gridrec', filter_name='parzen', nchunk=1) # Mask each reconstructed slice with a circle. rec = tomopy.circ_mask(rec, axis=0, ratio=0.95) index = 0 # Save images to a temporary folder. #fname = os.path.dirname(h5fname) + os.sep + 'try_rec/' + path_base_name(h5fname) + os.sep + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0] fname = os.path.dirname(h5fname) + os.sep + 'centers/' + path_base_name(h5fname) + os.sep + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0] for axis in np.arange(*center_range): rfname = fname + '_' + str('{0:.2f}'.format(axis) + '.tiff') dxchange.write_tiff(rec[index], fname=rfname, overwrite=True) index = index + 1 print("Reconstructions: ", fname)
import tomopy import dxchange if __name__ == '__main__': # Set path to the micro-CT data to reconstruct. fname = '../../../tomopy/data/tooth.h5' # Select the sinogram range to reconstruct. start = 0 end = 2 # Read the APS 2-BM 0r 32-ID raw data. proj, flat, dark = dxchange.read_aps_32id(fname, sino=(start, end)) # Set data collection angles as equally spaced between 0-180 degrees. theta = tomopy.angles(proj.shape[0]) # Set data collection angles as equally spaced between 0-180 degrees. proj = tomopy.normalize(proj, flat, dark) # Set data collection angles as equally spaced between 0-180 degrees. rot_center = tomopy.find_center(proj, theta, init=290, ind=0, tol=0.5) tomopy.minus_log(proj) # Reconstruct object using Gridrec algorithm. recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec') # Mask each reconstructed slice with a circle. recon = tomopy.circ_mask(recon, axis=0, ratio=0.95)
def fast_tomo_recon(argv): """ Reconstruct subset slices (sinograms) equally spaced within tomographic dataset """ logger = logging.getLogger('fast_tomopy.fast_tomo_recon') # Parse arguments passed to function parser = argparse.ArgumentParser() parser.add_argument('-i', '--input', type=str, help='path to input raw ' 'dataset', required=True) parser.add_argument('-o', '--output-file', type=str, help='full path to h5 output ' 'file', default=os.path.join(os.getcwd(), "fast-tomopy.h5")) parser.add_argument('-sn', '--sino-num', type=int, help='Number of slices ' 'to reconstruct', default=5) parser.add_argument('-a', '--algorithm', type=str, help='Reconstruction' ' algorithm', default='gridrec', choices=['art', 'bart', 'fbp', 'gridrec', 'mlem', 'ospml_hybrid', 'ospml_quad', 'pml_hybrid', 'pml_quad', 'sirt']) parser.add_argument('-c', '--center', type=float, help='Center of rotation', default=None) parser.add_argument('-fn', '--filter-name', type=str, help='Name of filter' ' used for reconstruction', choices=['none', 'shepp', 'cosine', 'hann', 'hamming', 'ramlak', 'parzen', 'butterworth'], default='butterworth') parser.add_argument('-rr', '--ring-remove', type=str, help='Ring removal ' 'method', choices=['Octopus', 'Tomopy-FW', 'Tomopy-T'], default='Tomopy-FW') parser.add_argument('-lf', '--log-file', type=str, help='log file name', default='fast-tomopy.log') args = parser.parse_args() fh = logging.FileHandler(args.log_file) fh.setLevel(logging.INFO) fh.setFormatter(formatter) logger.addHandler(fh) if os.path.isdir(os.path.dirname(args.output_file)) is False: raise IOError(2, 'Directory of output file does not exist', args.output_file) # Read file metadata logger.info('Reading input file metadata') fdata, gdata = tomopy.read_als_832h5_metadata(args.input) proj_total = int(gdata['nangles']) last = proj_total - 1 sino_total = int(gdata['nslices']) ray_total = int(gdata['nrays']) px_size = float(gdata['pxsize'])/10 # cm # Set parameters for sinograms to read step = sino_total // (args.sino_num + 2) start = step end = step*(args.sino_num + 1) sino = (start, end, step) # Read full first and last projection to determine center of rotation if args.center is None: logger.info('Reading full first and last projection for COR') first_last, flats, darks, floc = tomopy.read_als_832h5(args.input, ind_tomo=(0, last)) first_last = tomopy.normalize_nf(first_last, flats, darks, floc) args.center = tomopy.find_center_pc(first_last[0, :, :], first_last[1, :, :], tol=0.1) logger.info('Detected center: %f', args.center) # Read and normalize raw sinograms logger.info('Reading raw data') tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino) logger.info('Normalizing raw data') tomo = tomopy.normalize_nf(tomo, flats, darks, floc) # Remove stripes from sinograms (remove rings) logger.info('Preprocessing normalized data') if args.ring_remove == 'Tomopy-FW': logger.info('Removing stripes from sinograms with %s', args.ring_remove) tomo = tomopy.remove_stripe_fw(tomo) elif args.ring_remove == 'Tomopy-T': logger.info('Removing stripes from sinograms with %s', args.ring_remove) tomo = tomopy.remove_stripe_ti(tomo) # Pad sinograms with edge values npad = int(np.ceil(ray_total*np.sqrt(2)) - ray_total)//2 tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge') args.center += npad # account for padding filter_name = np.array(args.filter_name, dtype=(str, 16)) theta = tomopy.angles(tomo.shape[0], 270, 90) logger.info('Reconstructing normalized data') # Reconstruct sinograms rec = tomopy.recon(tomo, theta, center=args.center, emission=False, algorithm=args.algorithm, filter_name=filter_name) rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0) rec = rec/px_size # Remove rings from reconstruction if args.ring_remove == 'Octopus': logger.info('Removing rings from reconstructions with %s', args.ring_remove) thresh = float(gdata['ring_threshold']) thresh_max = float(gdata['upp_ring_value']) thresh_min = float(gdata['low_ring_value']) theta_min = int(gdata['max_arc_length']) rwidth = int(gdata['max_ring_size']) rec = tomopy.remove_rings(rec, center_x=args.center, thresh=thresh, thresh_max=thresh_max, thresh_min=thresh_min, theta_min=theta_min, rwidth=rwidth) # Write reconstruction data to new hdf5 file fdata['stage'] = 'fast-tomopy' fdata['stage_flow'] = '/raw/' + fdata['stage'] fdata['stage_version'] = 'fast-tomopy-0.1' # WHAT ABOUT uuid ????? Who asigns this??? del fdata['uuid'] # I'll get rid of it altogether then... gdata['Reconstruction_Type'] = 'tomopy-gridrec' gdata['ring_removal_method'] = args.ring_remove gdata['rfilter'] = args.filter_name logger.info('Writing reconstructed data to h5 file') write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step) return