# Copyright 2019 United Kingdom Research and Innovation
# Copyright 2019 The University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Authors:
# CIL Developers, listed at: https://github.com/TomographicImaging/CIL/blob/master/NOTICE.txt
from cil.framework import ImageGeometry
from cil.framework.labels import ImageDimension
import numpy
import numpy as np
from PIL import Image
import os
import os.path
import sys
from zipfile import ZipFile
from scipy.io import loadmat
from cil.io import NEXUSDataReader, NikonDataReader, ZEISSDataReader
from zenodo_get import zenodo_get
class DATA(object):
@classmethod
def dfile(cls):
return None
class CILDATA(DATA):
data_dir = os.path.abspath(os.path.join(sys.prefix, 'share','cil'))
@classmethod
def get(cls, size=None, scale=(0,1), **kwargs):
ddir = kwargs.get('data_dir', CILDATA.data_dir)
loader = TestData(data_dir=ddir)
return loader.load(cls.dfile(), size, scale, **kwargs)
class REMOTEDATA(DATA):
FOLDER = ''
ZENODO_RECORD = ''
ZIP_FILE = ''
@classmethod
def get(cls, data_dir):
return None
@classmethod
def download_data(cls, data_dir, prompt=True):
'''
Download a dataset from a remote repository
Parameters
----------
data_dir: str, optional
The path to the data directory where the downloaded data should be stored
'''
if os.path.isdir(os.path.join(data_dir, cls.FOLDER)):
print("Dataset folder already exists in " + data_dir)
else:
user_input = input("Are you sure you want to download {cls.ZIP_FILE} dataset from Zenodo record {cls.ZENODO_RECORD}? [Y/n]: ") if prompt else 'y'
if user_input.lower() not in ('y', 'yes'):
print('Download cancelled')
return False
zenodo_get([cls.ZENODO_RECORD, '-g', cls.ZIP_FILE, '-o', data_dir])
with ZipFile(os.path.join(data_dir, cls.ZIP_FILE), 'r') as zip_ref:
zip_ref.extractall(os.path.join(data_dir, cls.FOLDER))
os.remove(os.path.join(data_dir, cls.ZIP_FILE))
return True
class BOAT(CILDATA):
@classmethod
def dfile(cls):
return TestData.BOAT
class CAMERA(CILDATA):
@classmethod
def dfile(cls):
return TestData.CAMERA
class PEPPERS(CILDATA):
@classmethod
def dfile(cls):
return TestData.PEPPERS
class RESOLUTION_CHART(CILDATA):
@classmethod
def dfile(cls):
return TestData.RESOLUTION_CHART
class SIMPLE_PHANTOM_2D(CILDATA):
@classmethod
def dfile(cls):
return TestData.SIMPLE_PHANTOM_2D
class SHAPES(CILDATA):
@classmethod
def dfile(cls):
return TestData.SHAPES
class RAINBOW(CILDATA):
@classmethod
def dfile(cls):
return TestData.RAINBOW
[docs]
class SYNCHROTRON_PARALLEL_BEAM_DATA(CILDATA):
[docs]
@classmethod
def get(cls, **kwargs):
'''
A DLS dataset
Parameters
----------
data_dir: str, optional
The path to the data directory
Returns
-------
AcquisitionData
The DLS dataset
'''
ddir = kwargs.get('data_dir', CILDATA.data_dir)
loader = NEXUSDataReader()
loader.set_up(file_name=os.path.join(os.path.abspath(ddir), '24737_fd_normalised.nxs'))
return loader.read()
[docs]
class SIMULATED_PARALLEL_BEAM_DATA(CILDATA):
[docs]
@classmethod
def get(cls, **kwargs):
'''
A simulated parallel-beam dataset generated from SIMULATED_SPHERE_VOLUME
Parameters
----------
data_dir: str, optional
The path to the data directory
Returns
-------
AcquisitionData
The simulated spheres dataset
'''
ddir = kwargs.get('data_dir', CILDATA.data_dir)
loader = NEXUSDataReader()
loader.set_up(file_name=os.path.join(os.path.abspath(ddir), 'sim_parallel_beam.nxs'))
return loader.read()
[docs]
class SIMULATED_CONE_BEAM_DATA(CILDATA):
[docs]
@classmethod
def get(cls, **kwargs):
'''
A cone-beam dataset generated from SIMULATED_SPHERE_VOLUME
Parameters
----------
data_dir: str, optional
The path to the data directory
Returns
-------
AcquisitionData
The simulated spheres dataset
'''
ddir = kwargs.get('data_dir', CILDATA.data_dir)
loader = NEXUSDataReader()
loader.set_up(file_name=os.path.join(os.path.abspath(ddir), 'sim_cone_beam.nxs'))
return loader.read()
class SIMULATED_SPHERE_VOLUME(CILDATA):
@classmethod
def get(cls, **kwargs):
'''
A simulated volume of spheres
Parameters
----------
data_dir: str, optional
The path to the data directory
Returns
-------
ImageData
The simulated spheres volume
'''
ddir = kwargs.get('data_dir', CILDATA.data_dir)
loader = NEXUSDataReader()
loader.set_up(file_name=os.path.join(os.path.abspath(ddir), 'sim_volume.nxs'))
return loader.read()
[docs]
class WALNUT(REMOTEDATA):
'''
A microcomputed tomography dataset of a walnut from https://zenodo.org/records/4822516
Example
--------
>>> data_dir = 'my_PC/data_folder'
>>> dataexample.WALNUT.download_data(data_dir) # download the data
>>> dataexample.WALNUT.get(data_dir) # load the data
'''
FOLDER = 'walnut'
ZENODO_RECORD = '4822516'
ZIP_FILE = 'walnut.zip'
[docs]
@classmethod
def get(cls, data_dir):
'''
Get the microcomputed tomography dataset of a walnut from https://zenodo.org/records/4822516
This function returns the raw projection data from the .txrm file
Parameters
----------
data_dir: str
The path to the directory where the dataset is stored. Data can be downloaded with dataexample.WALNUT.download_data(data_dir)
Returns
-------
ImageData
The walnut dataset
'''
filepath = os.path.join(data_dir, cls.FOLDER, 'valnut','valnut_2014-03-21_643_28','tomo-A','valnut_tomo-A.txrm')
try:
loader = ZEISSDataReader(file_name=filepath)
return loader.read()
except(FileNotFoundError):
raise(FileNotFoundError("Dataset .txrm file not found in specifed data_dir: {} \n \
Specify a different data_dir or download data with dataexample.{}.download_data(data_dir)".format(filepath, cls.__name__)))
[docs]
class USB(REMOTEDATA):
'''
A microcomputed tomography dataset of a usb memory stick from https://zenodo.org/records/4822516
Example
--------
>>> data_dir = 'my_PC/data_folder'
>>> dataexample.USB.download_data(data_dir) # download the data
>>> dataexample.USB.get(data_dir) # load the data
'''
FOLDER = 'USB'
ZENODO_RECORD = '4822516'
ZIP_FILE = 'usb.zip'
[docs]
@classmethod
def get(cls, data_dir):
'''
Get the microcomputed tomography dataset of a usb memory stick from https://zenodo.org/records/4822516
This function returns the raw projection data from the .txrm file
Parameters
----------
data_dir: str
The path to the directory where the dataset is stored. Data can be downloaded with dataexample.WALNUT.download_data(data_dir)
Returns
-------
ImageData
The usb dataset
'''
filepath = os.path.join(data_dir, cls.FOLDER, 'gruppe 4','gruppe 4_2014-03-20_1404_12','tomo-A','gruppe 4_tomo-A.txrm')
try:
loader = ZEISSDataReader(file_name=filepath)
return loader.read()
except(FileNotFoundError):
raise(FileNotFoundError("Dataset .txrm file not found in: {} \n \
Specify a different data_dir or download data with dataexample.{}.download_data(data_dir)".format(filepath, cls.__name__)))
[docs]
class KORN(REMOTEDATA):
'''
A microcomputed tomography dataset of a sunflower seeds in a box from https://zenodo.org/records/6874123
Example
--------
>>> data_dir = 'my_PC/data_folder'
>>> dataexample.KORN.download_data(data_dir) # download the data
>>> dataexample.KORN.get(data_dir) # load the data
'''
FOLDER = 'korn'
ZENODO_RECORD = '6874123'
ZIP_FILE = 'korn.zip'
[docs]
@classmethod
def get(cls, data_dir):
'''
Get the microcomputed tomography dataset of a sunflower seeds in a box from https://zenodo.org/records/6874123
This function returns the raw projection data from the .xtekct file
Parameters
----------
data_dir: str
The path to the directory where the dataset is stored. Data can be downloaded with dataexample.KORN.download_data(data_dir)
Returns
-------
ImageData
The korn dataset
'''
filepath = os.path.join(data_dir, cls.FOLDER, 'Korn i kasse','47209 testscan korn01_recon.xtekct')
try:
loader = NikonDataReader(file_name=filepath)
return loader.read()
except(FileNotFoundError):
raise(FileNotFoundError("Dataset .xtekct file not found in: {} \n \
Specify a different data_dir or download data with dataexample.{}.download_data(data_dir)".format(filepath, cls.__name__)))
[docs]
class SANDSTONE(REMOTEDATA):
'''
A synchrotron x-ray tomography dataset of sandstone from https://zenodo.org/records/4912435
A small subset of the data containing selected projections and 4 slices of the reconstruction
Example
--------
>>> data_dir = 'my_PC/data_folder'
>>> dataexample.SANDSTONE.download_data(data_dir) # download the data
>>> dataexample.SANDSTONE.get(data_dir) # load the data
'''
FOLDER = 'sandstone'
ZENODO_RECORD = '4912435'
ZIP_FILE = 'small.zip'
[docs]
@classmethod
def get(cls, data_dir, filename):
'''
Get the synchrotron x-ray tomography dataset of sandstone from https://zenodo.org/records/4912435
A small subset of the data containing selected projections and 4 slices of the reconstruction
Parameters
----------
data_dir: str
The path to the directory where the dataset is stored. Data can be downloaded with dataexample.SANDSTONE.download_data(data_dir)
file: str
The slices or projections to return, specify the path to the file within the data_dir
Returns
-------
ImageData
The selected sandstone dataset
'''
extension = os.path.splitext(filename)[1]
if extension == '.mat':
return loadmat(os.path.join(data_dir,filename))
raise KeyError(f"Unknown extension: {extension}")
[docs]
class TestData(object):
'''Class to return test data
provides 6 dataset:
BOAT = 'boat.tiff'
CAMERA = 'camera.png'
PEPPERS = 'peppers.tiff'
RESOLUTION_CHART = 'resolution_chart.tiff'
SIMPLE_PHANTOM_2D = 'hotdog'
SHAPES = 'shapes.png'
RAINBOW = 'rainbow.png'
'''
BOAT = 'boat.tiff'
CAMERA = 'camera.png'
PEPPERS = 'peppers.tiff'
RESOLUTION_CHART = 'resolution_chart.tiff'
SIMPLE_PHANTOM_2D = 'hotdog'
SHAPES = 'shapes.png'
RAINBOW = 'rainbow.png'
def __init__(self, data_dir):
self.data_dir = data_dir
[docs]
def load(self, which, size=None, scale=(0,1), **kwargs):
'''
Return a test data of the requested image
Parameters
----------
which: str
Image selector: BOAT, CAMERA, PEPPERS, RESOLUTION_CHART, SIMPLE_PHANTOM_2D, SHAPES, RAINBOW
size: tuple, optional
The size of the returned ImageData. If None default will be used for each image type
scale: tuple, optional
The scale of the data values
Returns
-------
ImageData
The simulated spheres volume
'''
if which not in [TestData.BOAT, TestData.CAMERA,
TestData.PEPPERS, TestData.RESOLUTION_CHART,
TestData.SIMPLE_PHANTOM_2D, TestData.SHAPES,
TestData.RAINBOW]:
raise ValueError('Unknown TestData {}.'.format(which))
if which == TestData.SIMPLE_PHANTOM_2D:
if size is None:
N = 512
M = 512
else:
N = size[0]
M = size[1]
sdata = numpy.zeros((N, M))
sdata[int(round(N/4)):int(round(3*N/4)), int(round(M/4)):int(round(3*M/4))] = 0.5
sdata[int(round(N/8)):int(round(7*N/8)), int(round(3*M/8)):int(round(5*M/8))] = 1
ig = ImageGeometry(voxel_num_x = M, voxel_num_y = N, dimension_labels=[ImageDimension.HORIZONTAL_Y, ImageDimension.HORIZONTAL_X])
data = ig.allocate()
data.fill(sdata)
elif which == TestData.SHAPES:
with Image.open(os.path.join(self.data_dir, which)) as f:
if size is None:
N = 200
M = 300
else:
N = size[0]
M = size[1]
ig = ImageGeometry(voxel_num_x = M, voxel_num_y = N, dimension_labels=[ImageDimension.HORIZONTAL_Y, ImageDimension.HORIZONTAL_X])
data = ig.allocate()
tmp = numpy.array(f.convert('L').resize((M,N)))
data.fill(tmp/numpy.max(tmp))
else:
with Image.open(os.path.join(self.data_dir, which)) as tmp:
if size is None:
N = tmp.size[1]
M = tmp.size[0]
else:
N = size[0]
M = size[1]
bands = tmp.getbands()
if len(bands) > 1:
if len(bands) == 4:
tmp = tmp.convert('RGB')
bands = tmp.getbands()
ig = ImageGeometry(voxel_num_x=M, voxel_num_y=N, channels=len(bands),
dimension_labels=[ImageDimension.HORIZONTAL_Y, ImageDimension.HORIZONTAL_X,ImageDimension.CHANNEL])
data = ig.allocate()
data.fill(numpy.array(tmp.resize((M,N))))
data.reorder([ImageDimension.CHANNEL,ImageDimension.HORIZONTAL_Y, ImageDimension.HORIZONTAL_X])
data.geometry.channel_labels = bands
else:
ig = ImageGeometry(voxel_num_x = M, voxel_num_y = N, dimension_labels=[ImageDimension.HORIZONTAL_Y, ImageDimension.HORIZONTAL_X])
data = ig.allocate()
data.fill(numpy.array(tmp.resize((M,N))))
if scale is not None:
dmax = data.as_array().max()
dmin = data.as_array().min()
# scale 0,1
data = (data -dmin) / (dmax - dmin)
if scale != (0,1):
#data = (data-dmin)/(dmax-dmin) * (scale[1]-scale[0]) +scale[0])
data *= (scale[1]-scale[0])
data += scale[0]
# print ("data.geometry", data.geometry)
return data
[docs]
@staticmethod
def random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs):
'''Function to add noise to input image
:param image: input dataset, DataContainer of numpy.ndarray
:param mode: type of noise
:param seed: seed for random number generator
:param clip: should clip the data.
See https://github.com/scikit-image/scikit-image/blob/master/skimage/util/noise.py
'''
if hasattr(image, 'as_array'):
arr = TestData.scikit_random_noise(image.as_array(), mode=mode, seed=seed, clip=clip,
**kwargs)
out = image.copy()
out.fill(arr)
return out
elif issubclass(type(image), numpy.ndarray):
return TestData.scikit_random_noise(image, mode=mode, seed=seed, clip=clip,
**kwargs)
[docs]
@staticmethod
def scikit_random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs):
"""
Function to add random noise of various types to a floating-point image.
Parameters
----------
image : ndarray
Input image data. Will be converted to float.
mode : str, optional
One of the following strings, selecting the type of noise to add:
- 'gaussian' Gaussian-distributed additive noise.
- 'localvar' Gaussian-distributed additive noise, with specified
local variance at each point of `image`.
- 'poisson' Poisson-distributed noise generated from the data.
- 'salt' Replaces random pixels with 1.
- 'pepper' Replaces random pixels with 0 (for unsigned images) or
-1 (for signed images).
- 's&p' Replaces random pixels with either 1 or `low_val`, where
`low_val` is 0 for unsigned images or -1 for signed
images.
- 'speckle' Multiplicative noise using out = image + n*image, where
n is uniform noise with specified mean & variance.
seed : int, optional
If provided, this will set the random seed before generating noise,
for valid pseudo-random comparisons.
clip : bool, optional
If True (default), the output will be clipped after noise applied
for modes `'speckle'`, `'poisson'`, and `'gaussian'`. This is
needed to maintain the proper image data range. If False, clipping
is not applied, and the output may extend beyond the range [-1, 1].
mean : float, optional
Mean of random distribution. Used in 'gaussian' and 'speckle'.
Default : 0.
var : float, optional
Variance of random distribution. Used in 'gaussian' and 'speckle'.
Note: variance = (standard deviation) ** 2. Default : 0.01
local_vars : ndarray, optional
Array of positive floats, same shape as `image`, defining the local
variance at every image point. Used in 'localvar'.
amount : float, optional
Proportion of image pixels to replace with noise on range [0, 1].
Used in 'salt', 'pepper', and 'salt & pepper'. Default : 0.05
salt_vs_pepper : float, optional
Proportion of salt vs. pepper noise for 's&p' on range [0, 1].
Higher values represent more salt. Default : 0.5 (equal amounts)
Returns
-------
out : ndarray
Output floating-point image data on range [0, 1] or [-1, 1] if the
input `image` was unsigned or signed, respectively.
Notes
-----
Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside
the valid image range. The default is to clip (not alias) these values,
but they may be preserved by setting `clip=False`. Note that in this case
the output may contain values outside the ranges [0, 1] or [-1, 1].
Use this option with care.
Because of the prevalence of exclusively positive floating-point images in
intermediate calculations, it is not possible to intuit if an input is
signed based on dtype alone. Instead, negative values are explicitly
searched for. Only if found does this function assume signed input.
Unexpected results only occur in rare, poorly exposes cases (e.g. if all
values are above 50 percent gray in a signed `image`). In this event,
manually scaling the input to the positive domain will solve the problem.
The Poisson distribution is only defined for positive integers. To apply
this noise type, the number of unique values in the image is found and
the next round power of two is used to scale up the floating-point result,
after which it is scaled back down to the floating-point image range.
To generate Poisson noise against a signed image, the signed image is
temporarily converted to an unsigned image in the floating point domain,
Poisson noise is generated, then it is returned to the original range.
This function is adapted from scikit-image.
https://github.com/scikit-image/scikit-image/blob/master/skimage/util/noise.py
Copyright (C) 2019, the scikit-image team
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the
distribution.
3. Neither the name of skimage nor the names of its contributors may be
used to endorse or promote products derived from this software without
specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
POSSIBILITY OF SUCH DAMAGE.
"""
mode = mode.lower()
# Detect if a signed image was input
if image.min() < 0:
low_clip = -1.
else:
low_clip = 0.
image = numpy.asarray(image, dtype=(np.float64))
if seed is not None:
np.random.seed(seed=seed)
allowedtypes = {
'gaussian': 'gaussian_values',
'localvar': 'localvar_values',
'poisson': 'poisson_values',
'salt': 'sp_values',
'pepper': 'sp_values',
's&p': 's&p_values',
'speckle': 'gaussian_values'}
kwdefaults = {
'mean': 0.,
'var': 0.01,
'amount': 0.05,
'salt_vs_pepper': 0.5,
'local_vars': np.zeros_like(image) + 0.01}
allowedkwargs = {
'gaussian_values': ['mean', 'var'],
'localvar_values': ['local_vars'],
'sp_values': ['amount'],
's&p_values': ['amount', 'salt_vs_pepper'],
'poisson_values': []}
for key in kwargs:
if key not in allowedkwargs[allowedtypes[mode]]:
raise ValueError('%s keyword not in allowed keywords %s' %
(key, allowedkwargs[allowedtypes[mode]]))
# Set kwarg defaults
for kw in allowedkwargs[allowedtypes[mode]]:
kwargs.setdefault(kw, kwdefaults[kw])
if mode == 'gaussian':
noise = np.random.normal(kwargs['mean'], kwargs['var'] ** 0.5,
image.shape)
out = image + noise
elif mode == 'localvar':
# Ensure local variance input is correct
if (kwargs['local_vars'] <= 0).any():
raise ValueError('All values of `local_vars` must be > 0.')
# Safe shortcut usage broadcasts kwargs['local_vars'] as a ufunc
out = image + np.random.normal(0, kwargs['local_vars'] ** 0.5)
elif mode == 'poisson':
# Determine unique values in image & calculate the next power of two
vals = len(np.unique(image))
vals = 2 ** np.ceil(np.log2(vals))
# Ensure image is exclusively positive
if low_clip == -1.:
old_max = image.max()
image = (image + 1.) / (old_max + 1.)
# Generating noise for each unique value in image.
out = np.random.poisson(image * vals) / float(vals)
# Return image to original range if input was signed
if low_clip == -1.:
out = out * (old_max + 1.) - 1.
elif mode == 'salt':
# Re-call function with mode='s&p' and p=1 (all salt noise)
out = TestData.random_noise(image, mode='s&p', seed=seed,
amount=kwargs['amount'], salt_vs_pepper=1.)
elif mode == 'pepper':
# Re-call function with mode='s&p' and p=1 (all pepper noise)
out = TestData.random_noise(image, mode='s&p', seed=seed,
amount=kwargs['amount'], salt_vs_pepper=0.)
elif mode == 's&p':
out = image.copy()
p = kwargs['amount']
q = kwargs['salt_vs_pepper']
flipped = np.random.choice([True, False], size=image.shape,
p=[p, 1 - p])
salted = np.random.choice([True, False], size=image.shape,
p=[q, 1 - q])
peppered = ~salted
out[flipped & salted] = 1
out[flipped & peppered] = low_clip
elif mode == 'speckle':
noise = np.random.normal(kwargs['mean'], kwargs['var'] ** 0.5,
image.shape)
out = image + image * noise
# Clip back to original range, if necessary
if clip:
out = np.clip(out, low_clip, 1.0)
return out
