# 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:: mipmap
:platform: Unix
:synopsis: 'Mipmapping plugin (a pyramid-like data downampling). \
A plugin to downsample multidimensional data
.. moduleauthor:: Mark Basham & Daniil Kazantsev <scientificsoftware@diamond.ac.uk>
"""
import math
import h5py
import logging
import numpy as np
import skimage.measure as skim
from savu.plugins.plugin import Plugin
from savu.plugins.utils import register_plugin
from savu.plugins.driver.cpu_plugin import CpuPlugin
[docs]@register_plugin
class Mipmap(Plugin, CpuPlugin):
def __init__(self):
super(Mipmap, self).__init__("Mipmap")
[docs] def process_frames(self, data):
self.mode_dict = { 'mean' : np.mean,
'median': np.median,
'min' : np.min,
'max' : np.max }
if self.parameters['mode'] in self.mode_dict:
sampler = self.mode_dict[self.parameters['mode']]
else:
logging.warning("Unknown downsample mode. Using 'mean'.")
sampler = np.mean
inputMap = data[0]
res = [data[0]]
for i in range(1,self.parameters["n_mipmaps"]):
downsample = skim.block_reduce(inputMap, (2, 2, 2), sampler)
res.append(downsample)
inputMap = np.copy(downsample)
return res
[docs] def setup(self):
in_dataset, out_dataset = self.get_datasets()
in_pData, out_pData = self.get_plugin_datasets()
full_data_shape = list(in_dataset[0].get_shape())
axis_labels = in_dataset[0].get_axis_labels()
voxel_dims = \
[i for i, e in enumerate(axis_labels) if 'voxel' in list(e.keys())[0]]
# Sort out input data
max_frames = self.get_max_frames()
in_pData[0].plugin_data_setup('VOLUME_XZ', max_frames,
slice_axis='voxel_y')
# use this for 3D data (need to keep slice dimension)
out_dataset = self.get_out_datasets()
out_pData = self.get_plugin_out_datasets()
for i in range(len(out_dataset)):
shape = tuple([int(math.ceil(float(x)/(2**i))) if d in voxel_dims
else x for d, x in enumerate(full_data_shape)])
out_dataset[i].create_dataset(axis_labels=in_dataset[0],
patterns=in_dataset[0],
shape=shape)
out_pData[i].plugin_data_setup('VOLUME_XZ', max_frames // 2**i, slice_axis='voxel_y')
[docs] def nOutput_datasets(self):
n_mipmaps = self.parameters['n_mipmaps']
name = self.parameters['out_dataset_prefix']
self.parameters['out_datasets'] = \
['%s_%i' % (name, 2**i) for i in range(n_mipmaps)]
return n_mipmaps
[docs] def get_max_frames(self):
return 8
[docs] def fix_transport(self):
return 'hdf5'