#!/usr/bin/env python
# -*- coding: utf-8 -*-
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# Copyright (c) 2016, Francesco De Carlo #
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"""
Module for describing .....
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import os
import fnmatch
import dxchange
import numpy as np
import matplotlib as mpl
import matplotlib.pylab as pl
import matplotlib.widgets as wdg
import matplotlib.pyplot as plt
import skimage as ski
import skimage.segmentation as seg
import skimage.morphology as morth
import scipy.ndimage as ndi
import scipy
__authors__ = "Francesco De Carlo"
__copyright__ = "Copyright (c) 2017, Argonne National Laboratory"
__version__ = "0.0.1"
__all__ = ['load_raw',
'shutter_off',
'particle_bed_location',
'laser_on',
'scale_to_one',
'sobel_stack',
'label']
[docs]def load_raw(top, index_start):
"""
Function description.
Parameters
----------
parameter_01 : type
Description.
parameter_02 : type
Description.
parameter_03 : type
Description.
Returns
-------
return_01
Description.
"""
template = os.listdir(top)[1]
nfile = len(fnmatch.filter(os.listdir(top), '*.tif'))
index_end = index_start + nfile
ind_tomo = range(index_start, index_end)
fname = top + template
# Read the tiff raw data.
rdata = dxchange.read_tiff_stack(fname, ind=ind_tomo)
return rdata
[docs]def shutter_off(image, alpha=0.7, plot=False):
"""
Function description.
Parameters
----------
parameter_01 : type
Description.
parameter_02 : type
Description.
parameter_03 : type
Description.
Returns
-------
return_01
Description.
"""
flat_sum = np.sum(image[0, :, :])
nimages = image.shape[0]
for index in range(nimages):
image_sum = np.sum(image[index, :, :])
if image_sum < alpha * flat_sum :
return index
return None
[docs]def particle_bed_location(image, plot=False):
"""
Function description.
Parameters
----------
parameter_01 : type
Description.
parameter_02 : type
Description.
parameter_03 : type
Description.
Returns
-------
return_01
Description.
"""
edge = np.sum(image, axis=1)
x = np.arange(0, edge.shape[0], 1)
y = ndi.gaussian_filter(edge/float(np.amax(edge)), 5)
if plot:
plt.plot(x, y)
plt.show()
return np.abs(y - 0.5).argmin()
[docs]def laser_on(rdata, particle_bed_ref, alpha=1.0):
"""
Function description.
Parameters
----------
parameter_01 : type
Description.
parameter_02 : type
Description.
parameter_03 : type
Description.
Returns
-------
return_01
Description.
"""
nimages = rdata.shape[0]
status = np.empty(nimages)
# for index in range(nimages):
# ndata = rdata[index]
# edge = np.sum(ndata, axis=1)
# y = ndi.gaussian_filter(edge/float(np.amax(edge)), 5)
# particle_bed = np.abs(y - 0.5).argmin()
# if particle_bed <= particle_bed_ref :
# status[index] = False
# else:
# status[index] = True
# particle_bed_ref = particle_bed_ref * alpha
# return status
for index in range(nimages):
ndata = rdata[index]
edge = np.sum(ndata, axis=1)
y = ndi.gaussian_filter(edge/float(np.amax(edge)), 5)
particle_bed = np.abs(y - 0.5).argmin()
if particle_bed > particle_bed_ref * alpha :
return index
return None
[docs]def scale_to_one(ndata):
"""
Function description.
Parameters
----------
parameter_01 : type
Description.
parameter_02 : type
Description.
parameter_03 : type
Description.
Returns
-------
return_01
Description.
"""
ndata_max = np.amax(ndata)
ndata_min = np.amin(ndata)
nimages = ndata.shape[0]
for index in range(nimages):
# normalize between [0,1]
ndata_max = np.amax(ndata[index, :, :])
ndata_min = np.amin(ndata[index, :, :])
ndata[index, :, :] = (ndata[index, :, :] - ndata_min) / (ndata_max - ndata_min)
return ndata
[docs]def sobel_stack(ndata):
"""
Function description.
Parameters
----------
parameter_01 : type
Description.
parameter_02 : type
Description.
parameter_03 : type
Description.
Returns
-------
return_01
Description.
"""
nimages = ndata.shape[0]
for index in range(nimages):
ndata[index, :, :] = ski.filters.sobel(ndata[index, :, :])
return ndata
[docs]def label(ndata, blur_radius=1.0, threshold=1):
"""
Function description.
Parameters
----------
parameter_01 : type
Description.
parameter_02 : type
Description.
parameter_03 : type
Description.
Returns
-------
return_01
Description.
"""
nimages = ndata.shape[0]
for index in range(nimages):
ndata[index, :, :] = ndi.gaussian_filter(ndata[index, :, :], blur_radius)
ndata[index, :, :], nr_objects = scipy.ndimage.label(ndata[index, :, :] > threshold)
print ("Image %d contains %d particles" % (index, nr_objects))
# print(np.amin(ndata[index, :, :]), np.amax(ndata[index, :, :]), np.mean(ndata[index, :, :]))
return ndata, nr_objects