Source code for monai.deploy.operators.png_converter_operator

# Copyright 2021 MONAI Consortium
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from os import makedirs
from os.path import join

import monai.deploy.core as md
from monai.deploy.core import DataPath, ExecutionContext, Image, InputContext, IOType, Operator, OutputContext
from monai.deploy.operators.dicom_data_loader_operator import DICOMDataLoaderOperator
from monai.deploy.operators.dicom_series_to_volume_operator import DICOMSeriesToVolumeOperator
from monai.deploy.utils.importutil import optional_import

PILImage, _ = optional_import("PIL", name="Image")

[docs]@md.input("image", Image, IOType.IN_MEMORY) @md.output("image", DataPath, IOType.DISK) @md.env(pip_packages=["Pillow >= 8.0.0"]) class PNGConverterOperator(Operator): """ This operator writes out a 3D Volumetric Image to disk in a slice by slice manner """
[docs] def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext): input_path = op_input.get("image") output_dir = op_output.get().path output_dir.mkdir(parents=True, exist_ok=True) self.convert_and_save(input_path, output_dir)
[docs] def convert_and_save(self, image, path): """ extracts the slices in originally acquired direction (often axial) and saves then in PNG format slice by slice in the specified directory """ image_data = image.asnumpy() image_shape = image_data.shape num_images = image_shape[0] for i in range(0, num_images): input_data = image_data[i, :, :] pil_image = PILImage.fromarray(input_data) if pil_image.mode != "RGB": pil_image = pil_image.convert("RGB"), join(str(i) + ".png")))
def main(): from pathlib import Path current_file_dir = Path(__file__).parent.resolve() data_path = current_file_dir.joinpath("../../../examples/ai_spleen_seg_data/dcm") out_path = "png-output" makedirs(out_path, exist_ok=True) files = [] loader = DICOMDataLoaderOperator() loader._list_files( data_path, files, ) study_list = loader._load_data(files) series = study_list[0].get_all_series()[0] op1 = DICOMSeriesToVolumeOperator() op1.prepare_series(series) voxels = op1.generate_voxel_data(series) metadata = op1.create_metadata(series) image = op1.create_volumetric_image(voxels, metadata) op2 = PNGConverterOperator() op2.convert_and_save(image, out_path) print(f"The loaded series object properties:\n{series}") print(f"The converted Image object metadata:\n{metadata}") if __name__ == "__main__": main()