Quickstart¶
Analysis of fibrous 2D data¶
Open a grayscale image, perform segmentation, estimate porosity, analyze fiber orientation and diameters, and plot the results.
import numpy as np
from skimage import io, filters
from quanfima import morphology as mrph
from quanfima import visualization as vis
from quanfima import utils
img = io.imread('../../data/polymer_slice.tif')
th_val = filters.threshold_otsu(img)
img_seg = (img > th_val).astype(np.uint8)
# estiamte porosity
pr = mrph.calc_porosity(img_seg)
for k,v in pr.items():
print 'Porosity ({}): {}'.format(k, v)
# prepare data and analyze fibers
data, skeleton, skeleton_thick = utils.prepare_data(img_seg)
cskel, fskel, omap, dmap, ovals, dvals = \
mrph.estimate_fiber_properties(data, skeleton)
# plot results
vis.plot_orientation_map(omap, fskel, min_label=u'0°', max_label=u'180°',
figsize=(10,10),
name='2d_polymer',
output_dir='../../data/results')
vis.plot_diameter_map(dmap, cskel, figsize=(10,10), cmap='gist_rainbow',
name='2d_polymer',
output_dir='../../data/results')
>> Porosity (Material 1): 0.845488888889
Analysis of 3D data of fibrous material¶
Open a micro-CT dataset, perform slice-wise segmentation, estimate porosity, analyze 3D fiber orientation and diameters, and visualize the results.
import numpy as np
from skimage import filters
from quanfima import morphology as mrph
from quanfima import visualization as vis
from quanfima import utils
data = np.memmap('../../data/polymer3d_8bit_128x128x128.raw',
shape=(128,128,128), dtype=np.uint8, mode='r')
data_seg = np.zeros_like(data, dtype=np.uint8)
for i in xrange(data_seg.shape[0]):
th_val = filters.threshold_otsu(data[i])
data_seg[i] = (data[i] > th_val).astype(np.uint8)
# estimate porosity
pr = mrph.calc_porosity(data_seg)
for k,v in pr.items():
print 'Porosity ({}): {}'.format(k, v)
# prepare data and analyze fibers
pdata, pskel, pskel_thick = utils.prepare_data(data_seg)
oprops = mrph.estimate_tensor_parallel('polymer_orientation_w32', pskel,
pskel_thick, 32,
'../../data/results')
odata = np.load(oprops['output_path']).item()
lat, azth, skel = odata['lat'], odata['azth'], odata['skeleton']
dprops = mrph.estimate_diameter_single_run('polymer_diameter',
'../../data/results',
pdata, skel, lat, azth)
dmtr = np.load(dprops['output_path']).item()['diameter']
# plot results
vis.plot_3d_orientation_map('polymer_w32', lat, azth,
output_dir='../../data/results',
camera_azth=40.47,
camera_elev=32.5,
camera_fov=35.0,
camera_loc=(40.85, 46.32, 28.85),
camera_zoom=0.005124)
vis.plot_3d_diameter_map('polymer_w32', dmtr,
output_dir='../../data/results',
measure_quantity='vox',
camera_azth=40.47,
camera_elev=32.5,
camera_fov=35.0,
camera_loc=(40.85, 46.32, 28.85),
camera_zoom=0.005124,
cb_x_offset=5,
width=620)
>> Porosity (Material 1): 0.855631351471
Estimation of p-values¶
Estimate p-values between several groups of samples with corresponding measurements of some material’s property.
import pandas as pd
from quanfima import utils
prop_vals = [174.93, 182.42, 194.61, 234.6, 229.73, 242.6, 38.78, 37.79,
32.06, 14.81, 15.23, 13.84]
mat_groups = ['PCL_cl', 'PCL_cl', 'PCL_cl', 'PCL_wa', 'PCL_wa', 'PCL_wa',
'PCL_SiHA_cl', 'PCL_SiHA_cl', 'PCL_SiHA_cl', 'PCL_SiHA_wa',
'PCL_SiHA_wa', 'PCL_SiHA_wa']
df_elongation = pd.DataFrame({'elongation': prop_vals, 'type': mat_groups})
_, _ = utils.calculate_tukey_posthoc(df_elongation, 'elongation',
name='samples_elongation',
write=True,
output_dir='../../data/results')
>> Tukey post-hoc (elongation)
>> Multiple Comparison of Means - Tukey HSD,FWER=0.05
>> =========================================================
>> group1 group2 meandiff lower upper reject
>> ---------------------------------------------------------
>> PCL_SiHA_cl PCL_SiHA_wa -21.5833 -37.8384 -5.3282 True
>> PCL_SiHA_cl PCL_cl 147.7767 131.5216 164.0318 True
>> PCL_SiHA_cl PCL_wa 199.4333 183.1782 215.6884 True
>> PCL_SiHA_wa PCL_cl 169.36 153.1049 185.6151 True
>> PCL_SiHA_wa PCL_wa 221.0167 204.7616 237.2718 True
>> PCL_cl PCL_wa 51.6567 35.4016 67.9118 True
>> ---------------------------------------------------------
>> ['PCL_SiHA_cl' 'PCL_SiHA_wa' 'PCL_cl' 'PCL_wa']
>> PCL_SiHA_cl-PCL_SiHA_wa : 0.011919282004
>> PCL_SiHA_wa-PCL_cl : 0.001
>> PCL_SiHA_wa-PCL_wa : 0.001
>> PCL_cl-PCL_wa : 0.001
>> PCL_SiHA_cl-PCL_wa : 0.001
>> PCL_SiHA_cl-PCL_cl : 0.001
Simulate and count particles in 3D data¶
Count and estimate properties of particles in a generated dataset comprised of spheres of varying radius.
from quanfima import simulation
from quanfima import morphology as mrph
volume, diameter, _, _ = simulation.simulate_particles((512,512,512),
n_particles=1000)
stats, labeled_volume = mrph.object_counter(volume)
>> Label Area Perimeter Sphericity
>> 0 1.0 20479.0 2896.958728 1.249533
>> 1 2.0 5575.0 1184.028571 1.284158
>> 2 3.0 57777.0 5816.142853 1.242660
>> 3 4.0 17077.0 2545.194226 1.260001
>> 4 5.0 5575.0 1184.028571 1.284158
>> 5 6.0 65267.0 6348.926691 1.234752
>> .. ... ... ... ...
>> 791 792.0 2109.0 605.185858 1.314154
>> 792 793.0 257.0 134.225397 1.456369
>> 793 794.0 257.0 134.225397 1.456369
>> 794 795.0 123.0 78.627417 1.521179
>> [795 rows x 4 columns]
Simulate fibers and estimate properties¶
Simulate a 3D dataset containing some number of fibers, estimate their properties and visualize.
import numpy as np
from scipy import ndimage as ndi
from skimage import morphology
from quanfima import simulation
from quanfima import morphology as mrph
from quanfima import utils
from quanfima import visualization as vis
volume, lat_ref, azth_ref, diameter, _, _ = \
simulation.simulate_fibers((128,128,128), n_fibers=30, max_fails=100,
radius_lim=(2, 3), gap_lim=(3,5))
volume = volume.astype(np.uint8)
volume = ndi.binary_fill_holes(volume)
volume = ndi.median_filter(volume, footprint=morphology.ball(2))
lat_ref = ndi.median_filter(lat_ref, footprint=morphology.ball(2))
azth_ref = ndi.median_filter(azth_ref, footprint=morphology.ball(2))
# prepare data and analyze fibers
pdata, pskel, pskel_thick = utils.prepare_data(volume)
oprops = mrph.estimate_tensor_parallel('dataset_orientation_w36',
pskel, pskel_thick, 36,
'../../data/results')
odata = np.load(oprops['output_path']).item()
lat, azth, skel = odata['lat'], odata['azth'], odata['skeleton']
dprops = mrph.estimate_diameter_single_run('dataset_diameter',
'../../data/results',
pdata, skel, lat, azth)
dmtr = np.load(dprops['output_path']).item()['diameter']
# plot results
vis.plot_3d_orientation_map('dataset_w36', lat, azth,
output_dir='../../data/results',
camera_azth=40.47,
camera_elev=32.5,
camera_fov=35.0,
camera_loc=(40.85, 46.32, 28.85),
camera_zoom=0.005124)
vis.plot_3d_diameter_map('dataset_w36', dmtr,
output_dir='../../data/results',
measure_quantity='vox',
camera_azth=40.47,
camera_elev=32.5,
camera_fov=35.0,
camera_loc=(40.85, 46.32, 28.85),
camera_zoom=0.005124,
cb_x_offset=5,
width=620)
