# Copyright 2019 Diamond Light Source Ltd.
#
# 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.
"""
.. module:: gmm_segment3d
:platform: Unix
:synopsis: Gaussian mixture models for classification-segmentation routine. Wrapper around scikit GMM function
.. moduleauthor:: Daniil Kazantsev <scientificsoftware@diamond.ac.uk>
"""
from savu.plugins.plugin import Plugin
from savu.plugins.driver.multi_threaded_plugin import MultiThreadedPlugin
from savu.plugins.utils import register_plugin
import numpy as np
from sklearn.mixture import GaussianMixture
[docs]@register_plugin
class GmmSegment3d(Plugin, MultiThreadedPlugin):
def __init__(self):
super(GmmSegment3d, self).__init__("GmmSegment3d")
[docs] def setup(self):
in_dataset, out_dataset = self.get_datasets()
out_dataset[0].create_dataset(in_dataset[0], dtype=np.uint8)
in_pData, out_pData = self.get_plugin_datasets()
getall = ["VOLUME_XZ", "voxel_y"]
in_pData[0].plugin_data_setup('VOLUME_3D', 'single', getall=getall)
out_pData[0].plugin_data_setup('VOLUME_3D', 'single', getall=getall)
[docs] def pre_process(self):
# extract given parameters
self.classes = self.parameters['classes']
[docs] def process_frames(self, data):
# Do GMM classification/segmentation first
dimensdata = data[0].ndim
if (dimensdata == 2):
(Nsize1, Nsize2) = np.shape(data[0])
Nsize3 = 1
if (dimensdata == 3):
(Nsize1, Nsize2, Nsize3) = np.shape(data[0])
inputdata = data[0].reshape((Nsize1*Nsize2*Nsize3), 1)/np.max(data[0])
#run classification and segmentation
classif = GaussianMixture(n_components=self.classes, covariance_type="tied")
classif.fit(inputdata)
cluster = classif.predict(inputdata)
segm = classif.means_[cluster]
if (dimensdata == 2):
segm = segm.reshape(Nsize1, Nsize3, Nsize2)
else:
segm = segm.reshape(Nsize1, Nsize2, Nsize3)
maskGMM = segm.astype(np.float64) / np.max(segm)
maskGMM = 255 * maskGMM # Now scale by 255
maskGMM = maskGMM.astype(np.uint8) # obtain the GMM mask
return [maskGMM]
[docs] def nOutput_datasets(self):
return 1