# Copyright 2020 University of New South Wales, University of Sydney, Ingham Institute

# 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.

import sys
import os
import pydicom
import SimpleITK as sitk
from loguru import logger

from platipy.backend import app, DataObject
from platipy.dicom.nifti_to_rtstruct.convert import convert_nifti


body_settings_defaults = {
    "outputContourName": "primitive_body_contour",
    "seed": [0, 0, 0],
    "lowerThreshold": -5000,
    "upperThreshold": -800,
    "vectorRadius": [1, 1, 1],
}


@app.register("Primitive Body Segmentation", default_settings=body_settings_defaults)
def primitive_body_segmentation(data_objects, working_dir, settings):

    logger.info("Running Primitive Body Segmentation")
    logger.info("Using settings: " + str(settings))

    output_objects = []
    for d in data_objects:
        logger.info("Running on data object: " + d.path)

        # Read the image series
        load_path = d.path
        if d.type == "DICOM":
            load_path = sitk.ImageSeriesReader().GetGDCMSeriesFileNames(d.path)

        img = sitk.ReadImage(load_path)

        # Region growing using Connected Threshold Image Filter
        seg_con = sitk.ConnectedThreshold(
            img,
            seedList=[tuple(settings["seed"])],
            lower=settings["lowerThreshold"],
            upper=settings["upperThreshold"],
        )

        # Clean up the segmentation
        vector_radius = tuple(settings["vectorRadius"])
        kernel = sitk.sitkBall
        seg_clean = sitk.BinaryMorphologicalClosing(seg_con, vector_radius, kernel)
        mask = sitk.BinaryNot(seg_clean)

        # Write the mask to a file in the working_dir
        mask_file = os.path.join(
            working_dir, "{0}.nii.gz".format(settings["outputContourName"])
        )
        sitk.WriteImage(mask, mask_file)

        # Create the output Data Object and add it to the list of output_objects
        data_object = DataObject(type="FILE", path=mask_file, parent=d)
        output_objects.append(data_object)

        # If the input was a DICOM, then we can use it to generate an output RTStruct
        if d.type == "DICOM":

            dicom_file = load_path[0]
            logger.info("Will write Dicom using file: {0}".format(dicom_file))
            masks = {settings["outputContourName"]: mask_file}

            # Use the image series UID for the file of the RTStruct
            suid = pydicom.dcmread(dicom_file).SeriesInstanceUID
            output_file = os.path.join(working_dir, "RS.{0}.dcm".format(suid))

            # Use the convert nifti function to generate RTStruct from nifti masks
            convert_nifti(dicom_file, masks, output_file)

            # Create the Data Object for the RTStruct and add it to the list
            do = DataObject(type="DICOM", path=output_file, parent=d)
            output_objects.append(do)

            logger.info("RTStruct generated")

    return output_objects


if __name__ == "__main__":

    # Run app by calling "python sample.py" from the command line

    dicom_listener_port = 7777
    dicom_listener_aetitle = "SAMPLE_SERVICE"

    app.run(
        debug=True,
        host="0.0.0.0",
        port=8000,
        dicom_listener_port=dicom_listener_port,
        dicom_listener_aetitle=dicom_listener_aetitle,
    )

# Copyright 2020 University of New South Wales, University of Sydney, Ingham Institute

# 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.

import sys
import os
import pydicom
import SimpleITK as sitk
from loguru import logger

from platipy.backend import app, DataObject
from platipy.dicom.nifti_to_rtstruct.convert import convert_nifti


body_settings_defaults = {
    "outputContourName": "primitive_body_contour",
    "seed": [0, 0, 0],
    "lowerThreshold": -5000,
    "upperThreshold": -800,
    "vectorRadius": [1, 1, 1],
}


@app.register("Primitive Body Segmentation", default_settings=body_settings_defaults)
def primitive_body_segmentation(data_objects, working_dir, settings):

    logger.info("Running Primitive Body Segmentation")
    logger.info("Using settings: " + str(settings))

    output_objects = []
    for d in data_objects:
        logger.info("Running on data object: " + d.path)

        # Read the image series
        load_path = d.path
        if d.type == "DICOM":
            load_path = sitk.ImageSeriesReader().GetGDCMSeriesFileNames(d.path)

        img = sitk.ReadImage(load_path)

        # Region growing using Connected Threshold Image Filter
        seg_con = sitk.ConnectedThreshold(
            img,
            seedList=[tuple(settings["seed"])],
            lower=settings["lowerThreshold"],
            upper=settings["upperThreshold"],
        )

        # Clean up the segmentation
        vector_radius = tuple(settings["vectorRadius"])
        kernel = sitk.sitkBall
        seg_clean = sitk.BinaryMorphologicalClosing(seg_con, vector_radius, kernel)
        mask = sitk.BinaryNot(seg_clean)

        # Write the mask to a file in the working_dir
        mask_file = os.path.join(
            working_dir, "{0}.nii.gz".format(settings["outputContourName"])
        )
        sitk.WriteImage(mask, mask_file)

        # Create the output Data Object and add it to the list of output_objects
        data_object = DataObject(type="FILE", path=mask_file, parent=d)
        output_objects.append(data_object)

        # If the input was a DICOM, then we can use it to generate an output RTStruct
        if d.type == "DICOM":

            dicom_file = load_path[0]
            logger.info("Will write Dicom using file: {0}".format(dicom_file))
            masks = {settings["outputContourName"]: mask_file}

            # Use the image series UID for the file of the RTStruct
            suid = pydicom.dcmread(dicom_file).SeriesInstanceUID
            output_file = os.path.join(working_dir, "RS.{0}.dcm".format(suid))

            # Use the convert nifti function to generate RTStruct from nifti masks
            convert_nifti(dicom_file, masks, output_file)

            # Create the Data Object for the RTStruct and add it to the list
            do = DataObject(type="DICOM", path=output_file, parent=d)
            output_objects.append(do)

            logger.info("RTStruct generated")

    return output_objects


if __name__ == "__main__":

    # Run app by calling "python sample.py" from the command line

    dicom_listener_port = 7777
    dicom_listener_aetitle = "SAMPLE_SERVICE"

    app.run(
        debug=True,
        host="0.0.0.0",
        port=8000,
        dicom_listener_port=dicom_listener_port,
        dicom_listener_aetitle=dicom_listener_aetitle,
    )

# Copyright 2020 University of New South Wales, University of Sydney, Ingham Institute # 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. # Read the image series # Region growing using Connected Threshold Image Filter # Clean up the segmentation # Write the mask to a file in the working_dir # Create the output Data Object and add it to the list of output_objects # If the input was a DICOM, then we can use it to generate an output RTStruct # Use the image series UID for the file of the RTStruct # Use the convert nifti function to generate RTStruct from nifti masks # Create the Data Object for the RTStruct and add it to the list # Run app by calling "python sample.py" from the command line