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Migrate the ClaraViz operator
Signed-off-by: M Q <mingmelvinq@nvidia.com>
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‎monai/deploy/operators/clara_viz_operator.py

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# # Copyright 2022 MONAI Consortium
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# # Licensed under the Apache License, Version 2.0 (the "License");
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# # you may not use this file except in compliance with the License.
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# # You may obtain a copy of the License at
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# # http://www.apache.org/licenses/LICENSE-2.0
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# # Unless required by applicable law or agreed to in writing, software
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# # distributed under the License is distributed on an "AS IS" BASIS,
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# # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# # See the License for the specific language governing permissions and
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# # limitations under the License.
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# import numpy as np
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# import monai.deploy.core as md
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# from monai.deploy.core import ExecutionContext, Image, InputContext, IOType, Operator, OutputContext
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# from monai.deploy.utils.importutil import optional_import
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# DataDefinition, _ = optional_import("clara.viz.core", name="DataDefinition")
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# Widget, _ = optional_import("clara.viz.widgets", name="Widget")
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# display, _ = optional_import("IPython.display", name="display")
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# interactive, _ = optional_import("ipywidgets", name="interactive")
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# Dropdown, _ = optional_import("ipywidgets", name="Dropdown")
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# Box, _ = optional_import("ipywidgets", name="Box")
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# VBox, _ = optional_import("ipywidgets", name="VBox")
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# @md.input("image", Image, IOType.IN_MEMORY)
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# @md.input("seg_image", Image, IOType.IN_MEMORY)
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# Copyright 2022-2023 MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from monai.deploy.core import Fragment, Image, Operator, OperatorSpec
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from monai.deploy.utils.importutil import optional_import
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DataDefinition, _ = optional_import("clara.viz.core", name="DataDefinition")
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Widget, _ = optional_import("clara.viz.widgets", name="Widget")
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display, _ = optional_import("IPython.display", name="display")
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interactive, _ = optional_import("ipywidgets", name="interactive")
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Dropdown, _ = optional_import("ipywidgets", name="Dropdown")
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Box, _ = optional_import("ipywidgets", name="Box")
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VBox, _ = optional_import("ipywidgets", name="VBox")
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# @md.env(pip_packages=["clara.viz.core", "clara.viz.widgets", "IPython"])
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# class ClaraVizOperator(Operator):
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# """
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# This operator uses Clara Viz to provide interactive view of a 3D volume including segmentation mask.
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# """
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# def __init__(self):
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# """Constructor of the operator."""
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# super().__init__()
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# @staticmethod
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# def _build_array(image, order):
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# numpy_array = image.asnumpy()
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# array = DataDefinition.Array(array=numpy_array, order=order)
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# array.element_size = [1.0]
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# array.element_size.append(image.metadata().get("col_pixel_spacing", 1.0))
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# array.element_size.append(image.metadata().get("row_pixel_spacing", 1.0))
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# array.element_size.append(image.metadata().get("depth_pixel_spacing", 1.0))
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# # the renderer is expecting data in RIP order (Right Inferior Posterior) which results in
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# # this matrix
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# target_affine_transform = [
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# [-1.0, 0.0, 0.0, 0.0],
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# [0.0, 0.0, 1.0, 0.0],
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# [0.0, -1.0, 0.0, 0.0],
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# [0.0, 0.0, 0.0, 1.0],
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# ]
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# dicom_affine_transform = image.metadata().get("dicom_affine_transform", np.identity(4))
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# affine_transform = np.matmul(target_affine_transform, dicom_affine_transform)
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# array.permute_axes = [
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# 0,
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# max(range(3), key=lambda k: abs(affine_transform[0][k])) + 1,
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# max(range(3), key=lambda k: abs(affine_transform[1][k])) + 1,
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# max(range(3), key=lambda k: abs(affine_transform[2][k])) + 1,
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# ]
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# array.flip_axes = [
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# False,
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# affine_transform[0][array.permute_axes[1] - 1] < 0.0,
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# affine_transform[1][array.permute_axes[2] - 1] < 0.0,
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# affine_transform[2][array.permute_axes[3] - 1] < 0.0,
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# ]
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# return array
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# def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):
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# """Displays the input image and segmentation mask
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# Args:
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# op_input (InputContext): An input context for the operator.
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# op_output (OutputContext): An output context for the operator.
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# context (ExecutionContext): An execution context for the operator.
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# """
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# input_image = op_input.get("image")
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# if not input_image:
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# raise ValueError("Input image is not found.")
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# input_seg_image = op_input.get("seg_image")
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# if not input_seg_image:
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# raise ValueError("Input segmentation image is not found.")
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# # build the data definition
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# data_definition = DataDefinition()
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# data_definition.arrays.append(self._build_array(input_image, "DXYZ"))
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# data_definition.arrays.append(self._build_array(input_seg_image, "MXYZ"))
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# widget = Widget()
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# widget.select_data_definition(data_definition)
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# # default view mode is 'CINEMATIC' switch to 'SLICE_SEGMENTATION' since we have no transfer functions defined
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# widget.settings["Views"][0]["mode"] = "SLICE_SEGMENTATION"
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# widget.settings["Views"][0]["cameraName"] = "Top"
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# widget.set_settings()
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# # add controls
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# def set_view_mode(view_mode):
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# widget.settings["Views"][0]["mode"] = view_mode
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# if view_mode == "CINEMATIC":
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# widget.settings["Views"][0]["cameraName"] = "Perspective"
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# elif widget.settings["Views"][0]["cameraName"] == "Perspective":
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# widget.settings["Views"][0]["cameraName"] = "Top"
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# widget.set_settings()
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# widget_view_mode = interactive(
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# set_view_mode,
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# view_mode=Dropdown(
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# options=[("Cinematic", "CINEMATIC"), ("Slice", "SLICE"), ("Slice Segmentation", "SLICE_SEGMENTATION")],
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# value="SLICE_SEGMENTATION",
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# description="View mode",
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# ),
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# )
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# def set_camera(camera):
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# if widget.settings["Views"][0]["mode"] != "CINEMATIC":
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# widget.settings["Views"][0]["cameraName"] = camera
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# widget.set_settings()
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# widget_camera = interactive(
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# set_camera, camera=Dropdown(options=["Top", "Right", "Front"], value="Top", description="Camera")
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# )
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# display(Box([widget, VBox([widget_view_mode, widget_camera])]))
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class ClaraVizOperator(Operator):
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"""
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This operator uses Clara Viz to provide interactive view of a 3D volume including segmentation mask.
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Named input(s):
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image: Image object of the input image, including key metadata, e.g. pixel spacings and orientations.
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seg_image: Image object of the segmentation image derived from the input image.
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"""
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def __init__(self, fragement: Fragment, *args, **kwargs):
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"""Constructor of the operator.
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Args:
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fragment (Fragment): An instance of the Application class which is derived from Fragment.
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"""
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self.input_name_image = "image"
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self.input_name_seg_image = "seg_image"
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super().__init__(fragement, *args, **kwargs)
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def setup(self, spec: OperatorSpec):
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spec.input(self.input_name_image)
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spec.input(self.input_name_seg_image)
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# There is no output for downstream receiver(s), but interactive UI.
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@staticmethod
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def _build_array(image, order):
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numpy_array = image.asnumpy()
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array = DataDefinition.Array(array=numpy_array, order=order)
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array.element_size = [1.0]
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array.element_size.append(image.metadata().get("col_pixel_spacing", 1.0))
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array.element_size.append(image.metadata().get("row_pixel_spacing", 1.0))
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array.element_size.append(image.metadata().get("depth_pixel_spacing", 1.0))
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# the renderer is expecting data in RIP order (Right Inferior Posterior) which results in
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# this matrix
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target_affine_transform = [
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[-1.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 1.0, 0.0],
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[0.0, -1.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 1.0],
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]
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dicom_affine_transform = image.metadata().get("dicom_affine_transform", np.identity(4))
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affine_transform = np.matmul(target_affine_transform, dicom_affine_transform)
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array.permute_axes = [
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0,
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max(range(3), key=lambda k: abs(affine_transform[0][k])) + 1,
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max(range(3), key=lambda k: abs(affine_transform[1][k])) + 1,
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max(range(3), key=lambda k: abs(affine_transform[2][k])) + 1,
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]
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array.flip_axes = [
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False,
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affine_transform[0][array.permute_axes[1] - 1] < 0.0,
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affine_transform[1][array.permute_axes[2] - 1] < 0.0,
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affine_transform[2][array.permute_axes[3] - 1] < 0.0,
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]
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return array
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def compute(self, op_input, op_output, context):
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"""Displays the input image and segmentation mask
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Args:
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op_input (InputContext): An input context for the operator.
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op_output (OutputContext): An output context for the operator.
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context (ExecutionContext): An execution context for the operator.
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"""
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input_image = op_input.receive(self.input_name_image)
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if not input_image or isinstance(input_image, Image):
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raise ValueError(f"Input image not found or its type {type(Image)} not Image.")
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input_seg_image = op_input.receive(self.input_name_seg_image)
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if not input_seg_image:
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raise ValueError(f"Input segmentation image not found or its type {type(Image)} not Image.")
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# build the data definition
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data_definition = DataDefinition()
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data_definition.arrays.append(self._build_array(input_image, "DXYZ"))
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data_definition.arrays.append(self._build_array(input_seg_image, "MXYZ"))
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widget = Widget()
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widget.select_data_definition(data_definition)
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# default view mode is 'CINEMATIC' switch to 'SLICE_SEGMENTATION' since we have no transfer functions defined
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widget.settings["Views"][0]["mode"] = "SLICE_SEGMENTATION"
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widget.settings["Views"][0]["cameraName"] = "Top"
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widget.set_settings()
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# add controls
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def set_view_mode(view_mode):
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widget.settings["Views"][0]["mode"] = view_mode
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if view_mode == "CINEMATIC":
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widget.settings["Views"][0]["cameraName"] = "Perspective"
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elif widget.settings["Views"][0]["cameraName"] == "Perspective":
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widget.settings["Views"][0]["cameraName"] = "Top"
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widget.set_settings()
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widget_view_mode = interactive(
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set_view_mode,
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view_mode=Dropdown(
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options=[("Cinematic", "CINEMATIC"), ("Slice", "SLICE"), ("Slice Segmentation", "SLICE_SEGMENTATION")],
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value="SLICE_SEGMENTATION",
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description="View mode",
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),
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)
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def set_camera(camera):
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if widget.settings["Views"][0]["mode"] != "CINEMATIC":
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widget.settings["Views"][0]["cameraName"] = camera
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widget.set_settings()
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widget_camera = interactive(
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set_camera, camera=Dropdown(options=["Top", "Right", "Front"], value="Top", description="Camera")
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)
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display(Box([widget, VBox([widget_view_mode, widget_camera])]))

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