#!/usr/bin/python3
#
# Copyright (c) 2014-2022 The Voxie Authors
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#

import numpy as np
import voxie

try:
    from skimage import filters
except ImportError:
    from skimage import filter as filters

args = voxie.parser.parse_args()
context = voxie.VoxieContext(args)
instance = context.createInstance()

if args.voxie_action != 'RunFilter':
    raise Exception('Invalid operation: ' + args.voxie_action)

with context.makeObject(context.bus, context.busName, args.voxie_operation, ['de.uni_stuttgart.Voxie.ExternalOperationRunFilter']).ClaimOperationAndCatch() as op:
    # print (op.Properties)
    inputData = op.GetInputData('de.uni_stuttgart.Voxie.Input').CastTo(
        'de.uni_stuttgart.Voxie.VolumeData')
    outputPath = op.Properties['de.uni_stuttgart.Voxie.Output'].getValue('o')

    method = op.Properties['de.uni_stuttgart.Voxie.Filter.BinarySegmentation.ThresholdMethod'].getValue(
        's')
    thresholdValue = op.Properties['de.uni_stuttgart.Voxie.Filter.BinarySegmentation.Threshold'].getValue(
        'd')
    negateOutput = op.Properties['de.uni_stuttgart.Voxie.Filter.BinarySegmentation.NegateOutput'].getValue(
        'b')

    inputDataVoxel = inputData.CastTo('de.uni_stuttgart.Voxie.VolumeDataVoxel')

    # TODO#output.DisplayName = "BinarySegmentation Data" # TODO: do this?

    # TODO: preview box

    with inputDataVoxel.GetBufferReadonly() as inputArray:
        with op.GetOutputVolumeDataVoxelLike(outputPath, inputDataVoxel, type=('bool', 8, 'native')) as data:
            # print ('XX', inputDataVoxel.GridSpacing, data.GridSpacing)
            with data.CreateUpdate() as update, data.GetBufferWritable(update) as outputBuffer:
                if method == 'de.uni_stuttgart.Voxie.Filter.BinarySegmentation.ThresholdMethod.Otsu':
                    threshold = filters.threshold_otsu(inputArray[:])
                elif method == 'de.uni_stuttgart.Voxie.Filter.BinarySegmentation.ThresholdMethod.Value':
                    threshold = thresholdValue
                else:
                    raise Exception(
                        'Unknown threshold method: ' + repr(method))
                # See Book with ISBN 978-3-662-49465-3
                # TODO: progress bar
                outputBuffer.array[:] = (threshold > inputArray[:]) if negateOutput else (
                    threshold <= inputArray[:])

                version = update.Finish()

            result = {}
            result[outputPath] = {
                'Data': voxie.Variant('o', data._objectPath),
                'DataVersion': voxie.Variant('o', version._objectPath),
            }
            op.Finish(result)
            version._referenceCountingObject.destroy()

context.client.destroy()
