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Contents

Performed segmentation

Warning

This notebook shows how to use a function that is poorly written and not optimized. The results from this function are also to be taken carefully.

This notebook show how to segment you data to find grain boundaries.

import xarrayaita.loadData_aita as lda #here are some function to build xarrayaita structure
import xarrayaita.aita as xa
import xarrayuvecs.uvecs as xu
import xarrayuvecs.lut2d as lut2d

import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np

import datetime
from skimage import morphology
from tqdm.notebook import tqdm
import scipy

%matplotlib widget

Load your data

# path to data and microstructure
path_data='orientation_test.dat'

data=lda.aita5col(path_data)

data

<xarray.Dataset>
Dimensions:      (uvecs: 2, x: 1000, y: 2500)
Coordinates:
  * x            (x) float64 0.0 0.02 0.04 0.06 0.08 ... 19.92 19.94 19.96 19.98
  * y            (y) float64 49.98 49.96 49.94 49.92 49.9 ... 0.06 0.04 0.02 0.0
Dimensions without coordinates: uvecs
Data variables:
    orientation  (y, x, uvecs) float64 2.395 0.6451 5.377 ... 0.6098 0.6473
    quality      (y, x) int64 0 90 92 93 92 92 94 94 ... 96 96 96 96 96 97 97 96
    micro        (y, x) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0
    grainId      (y, x) int64 1 1 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1 1
Attributes:
    date:       Thursday, 19 Nov 2015, 11:24 am
    unit:       millimeters
    step_size:  0.02
    path_dat:   orientation_test.dat

Segment the data

plt.figure(figsize=(5,10))
out=data.aita.interactive_segmentation()

The new xarray.DataSet.aita

out.ds

<xarray.Dataset>
Dimensions:      (uvecs: 2, x: 1000, y: 2500)
Coordinates:
  * x            (x) float64 0.0 0.02 0.04 0.06 0.08 ... 19.92 19.94 19.96 19.98
  * y            (y) float64 49.98 49.96 49.94 49.92 49.9 ... 0.06 0.04 0.02 0.0
Dimensions without coordinates: uvecs
Data variables:
    orientation  (y, x, uvecs) float64 2.395 0.6451 5.377 ... 0.6098 0.6473
    quality      (y, x) int64 0 90 92 93 92 92 94 94 ... 96 96 96 96 96 97 97 96
    micro        (y, x) bool True True True True True ... True True True True
    grainId      (y, x) int32 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0
Attributes:
    date:       Thursday, 19 Nov 2015, 11:24 am
    unit:       millimeters
    step_size:  0.02
    path_dat:   orientation_test.dat

Plot micro

%matplotlib inline

plt.figure(figsize=(5,10))
out.ds.micro.plot()

<matplotlib.collections.QuadMesh at 0x7f23c3d238e0>

Plot grainId

plt.figure(figsize=(5,10))
out.ds.grainId.plot()

<matplotlib.collections.QuadMesh at 0x7f23c3c5fb20>

The parameters for the segmentation

If you want to re-do the segmentation with the same value they can be find here :

print('Use scharr:',out.use_scharr)
print('Value scharr:',out.val_scharr)
print('Use canny:',out.use_canny)
print('Value canny:',out.val_canny)
print('Images canny:',out.img_canny)
print('Use quality:',out.use_quality)
print('Value quality:',out.val_quality)
print('Include border:',out.include_border)

Use scharr: True
Value scharr: 1.5
Use canny: True
Value canny: 1.5
Images canny: full color wheel
Use quality: True
Value quality: 60.0
Include border: True

Export segmentation

Export segmentation for manual correction

plt.imsave('micro auto.bmp', np.array(out.ds.micro), cmap=cm.gray)

The misorientation angle distribution The xarray.Dataset strucuture for AITA