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joelvbernierJun-Sang Park
joelvbernier
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Jun-Sang Park
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more nf mods
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‎scripts/nf_munge.py‎

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#!/usr/bin/env python2
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Jun 19 18:43:52 2018
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@author: s1iduser
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"""
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from skimage import io
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# %%
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img_stem = 'MapZBeam_5mmTo9mm_%s__%06d.tif'
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im_or = ['m90deg', '0deg', '90deg']
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im_idx = 31
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img_list = [io.imread(img_stem % (i, im_idx)) for i in im_or]
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# %%
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for i, img in enumerate(img_list):
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fig, ax = plt.subplots()
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ax.imshow(img, cmap=plt.cm.inferno, vmin=np.percentile(img, 50))
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fig.suptitle("%s" % im_or[i])
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ax.axis('normal')
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# %%
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x_range = [0, 2047] # eyeballed
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y_range = [1990, 2040] # eyeballed
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beam_img_list = [img[np.ix_(range(*y_range), range(*x_range))] for img in img_list]
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sinogram = np.vstack([np.sum(bimg, axis=0) for bimg in beam_img_list])
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# %%
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pix_size = 0.00148
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left = pix_size*np.r_[361, 2015]
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right = pix_size*np.r_[1669, 2029]
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diff = right - left
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incl = np.degrees(np.arctan2(diff[1], diff[0]))

‎scripts/process_nf_grain_map.py‎

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# name of the material for the reconstruction
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mat_name = 'MAT_NAME'
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#reconstruction with misorientation included, for many grains, this will quickly
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#make the reconstruction size unmanagable
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misorientation_bnd=0.0 #degrees
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misorientation_spacing=0.25 #degress
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# reconstruction with misorientation included, for many grains,
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# this will quickly make the reconstruction size unmanagable
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misorientation_bnd = 0.0 # degrees
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misorientation_spacing = 0.25 # degrees
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beam_stop_width=0.55#mm, assumed to be in the center of the detector
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beam_stop_width = 0.55 # mm, assumed to be in the center of the detector
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ome_range_deg = [(0., 360.), ] # degrees
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ome_range_deg=[(0.,360.)] #degrees
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# maximu bragg angle 2theta in degrees
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# if -1, all peaks that will hit the detector are calculated
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max_tth = -1.
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# image processing
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num_for_dark = 250 # num images to use for median data
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threshold = 3.
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max_tth=-1. #degrees, if a negative number is input, all peaks that will hit the detector are calculated
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# !!! DO NOT CHANGE ANY OF THESE UNLESS YOU KNOW WHAT YOU ARE DOING
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num_erosions = 3 # num iterations of images erosion
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num_dilations = 2 # num iterations of images erosion
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ome_dilation_iter = 1 # num iterations of 3d image stack dilations
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chunk_size = 500 # chunksize for multiprocessing
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#image processing
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num_for_dark=250#num images to use for median data
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threshold=3.
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num_erosions=3 #num iterations of images erosion, don't mess with unless you know what you're doing
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num_dilations=2 #num iterations of images erosion, don't mess with unless you know what you're doing
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ome_dilation_iter=1 #num iterations of 3d image stack dilations, don't mess with unless you know what you're doing
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# thresholds for accepting FF grains in NF reconstruction
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min_completeness = 0.5
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max_chisq = 0.05
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chunk_size=500#chunksize for multiprocessing, don't mess with unless you know what you're doing
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# tomography options
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layer_row = 1024 # row of layer to use to find the specimen cross section
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#thresholds for grains in reconstructions
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comp_thresh=0.5 #only use orientations from grains with completnesses ABOVE this threshold
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chi2_thresh=0.05 #only use orientations from grains BELOW this chi^2
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# TOMO OPTIONS
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# !!! Don't change these unless you know what you are doing
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# this will close small holes and remove noise
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recon_thresh = 0.00006 # usually varies between 0.0001 and 0.0005
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noise_obj_size = 5000
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min_hole_size = 5000
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# cross sectional to reconstruct (should be at least 20%-30% over sample width)
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cross_sectional_dim = 1.00
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#tomography options
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layer_row=1024 # row of layer to use to find the cross sectional specimen shape
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voxel_spacing = 0.005 # voxel spacing for the near field reconstruction in mm
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v_bnds = [-0.005, 0.005] # vertical (y) reconstruction voxel bounds in mm
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#Don't change these unless you know what you are doing, this will close small holes
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#and remove noise
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recon_thresh=0.00006#usually varies between 0.0001 and 0.0005
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noise_obj_size=5000
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min_hole_size=5000
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cross_sectional_dim=1.35 #cross sectional to reconstruct (should be at least 20%-30% over sample width)
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#voxel spacing for the near field reconstruction
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voxel_spacing=0.005#in mm
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##vertical (y) reconstruction voxel bounds in mm
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v_bnds=[-0.005,0.005]
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#==============================================================================
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# =============================================================================
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# %% LOAD GRAIN DATA
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#==============================================================================
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# =============================================================================
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experiment, nf_to_ff_id_map = nfutil.gen_trial_exp_data(grain_out_file,det_file,mat_file, x_ray_energy, mat_name, max_tth, comp_thresh, chi2_thresh, misorientation_bnd, \
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misorientation_spacing,ome_range_deg, num_imgs, beam_stop_width)
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experiment, nf_to_ff_id_map = nfutil.gen_trial_exp_data(
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grain_out_file, det_file, mat_file, x_ray_energy, mat_name,
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max_tth, min_completeness, max_chisq, misorientation_bnd,
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misorientation_spacing, ome_range_deg, num_imgs, beam_stop_width
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)
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#==============================================================================
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# =============================================================================
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# %% TOMO PROCESSING - GENERATE BRIGHT FIELD
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#==============================================================================
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# =============================================================================
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tbf=tomoutil.gen_bright_field(tbf_data_folder,tbf_img_start,tbf_num_imgs,experiment.nrows,experiment.ncols)
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tbf = tomoutil.gen_bright_field(
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tbf_data_folder, tbf_img_start, tbf_num_imgs,
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experiment.nrows, experiment.ncols
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)
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#==============================================================================
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# =============================================================================
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# %% TOMO PROCESSING - BUILD RADIOGRAPHS
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#==============================================================================
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rad_stack=tomoutil.gen_attenuation_rads(tomo_data_folder,tbf,tomo_img_start,tomo_num_imgs,experiment.nrows,experiment.ncols)
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# =============================================================================
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rad_stack = tomoutil.gen_attenuation_rads(
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tomo_data_folder, tbf, tomo_img_start, tomo_num_imgs,
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experiment.nrows, experiment.ncols
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)
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#==============================================================================
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# =============================================================================
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# %% TOMO PROCESSING - INVERT SINOGRAM
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#==============================================================================
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# =============================================================================
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reconstruction_fbp=tomoutil.tomo_reconstruct_layer(rad_stack,cross_sectional_dim,layer_row=layer_row,\
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start_tomo_ang=ome_range_deg[0][0],end_tomo_ang=ome_range_deg[0][1],\
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tomo_num_imgs=tomo_num_imgs, center=experiment.detector_params[3])
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reconstruction_fbp = tomoutil.tomo_reconstruct_layer(
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rad_stack, cross_sectional_dim, layer_row=layer_row,
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start_tomo_ang=ome_range_deg[0][0], end_tomo_ang=ome_range_deg[0][1],
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tomo_num_imgs=tomo_num_imgs, center=experiment.detector_params[3]
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)
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#==============================================================================
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# =============================================================================
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# %% TOMO PROCESSING - VIEW RAW FILTERED BACK PROJECTION
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#==============================================================================
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# =============================================================================
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plt.close('all')
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plt.imshow(reconstruction_fbp,vmin=0.75e-4,vmax=2e-4)
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#Use this image to view the raw reconstruction, estimate threshold levels. and
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#figure out if the rotation axis position needs to be corrected
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plt.imshow(reconstruction_fbp, vmin=0.75e-4, vmax=2e-4)
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# Use this image to view the raw reconstruction, estimate threshold levels. and
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# figure out if the rotation axis position needs to be corrected
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#==============================================================================
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# =============================================================================
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# %% TOMO PROCESSING - CLEAN TOMO RECONSTRUCTION
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#==============================================================================
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# =============================================================================
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165171
binary_recon = tomoutil.threshold_and_clean_tomo_layer(
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reconstruction_fbp,

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