scijava · ctrueden · Aug 9, 2024 · Aug 8, 2024 · Aug 8, 2024
diff --git a/docs/ops/doc/examples/mesh_viz.rst b/docs/ops/doc/examples/mesh_viz.rst
@@ -0,0 +1,92 @@
+=============================
+3D Analysis and Visualization
+=============================
+
+In this example, we will use SciJava Ops to construct a 3D mesh from a binary dataset, passing the result into `3D Viewer`_ for visualization. We use the `bat cochlea volume`_ dataset (more information `here <https://imagej.net/images/bat-cochlea-volume.txt>`_) from the ImageJ sample images, which users can either download using the link or open from the ``File → Open Samples → Bat Cochlea Volume`` menu selection within Fiji.
+
+.. figure:: https://media.scijava.org/scijava-ops/1.0.1/mesh-visualization.png
+
+    **Left**: The original binary bat cochlea volume, displayed as an Image. **Right**: The convex hull generated by SciJava Ops, overlaid on the original binary bat cochlea volume.
+
+
+.. TODO: Update SciJava Ops Image -> imglib2-mesh
+
+The following script accepts the binary dataset as its sole input, and creates the mesh using the `marching cubes`_ algorithm, which is included within SciJava Ops Image. We then use SciJava Ops to compute mesh volume, and then convert the mesh into a ``CustomTriangleMesh`` that can be passed to the 3DViewer.
+
+.. tabs::
+
+    .. code-tab:: scijava-groovy
+
+        #@ OpEnvironment ops
+        #@ UIService ui
+        #@ Dataset image
+        #@ ImagePlus imp
+        #@ StatusService status
+
+        import java.util.ArrayList
+        import java.util.List
+
+        import net.imagej.mesh.Mesh
+        import net.imagej.mesh.Triangle
+        import net.imglib2.RandomAccessibleInterval
+        import net.imglib2.type.BooleanType
+        import net.imglib2.util.Util
+
+        import org.scijava.vecmath.Point3f
+
+        import customnode.CustomTriangleMesh
+        import ij3d.Image3DUniverse
+
+        if (image.getType() instanceof BooleanType) {
+            // Input image is a binary image.
+            mask = image
+        }
+        else {
+            // Binarize the image using Otsu's threshold.
+            status.showStatus("Thresholding...")
+            bitType = ops.op("create.bit").producer().create()
+            mask = ops.op("create.img").input(image, bitType).apply()
+            ops.op("threshold.otsu").input(image).output(mask).compute()
+        }
+        println("Mask = $mask [type=${Util.getTypeFromInterval(mask).getClass().getName()}]")
+
+        //ui.show(mask)
+
+        // Compute surface mesh using marching cubes.
+        status.showStatus("Computing surface...")
+        mesh = ops.op("geom.marchingCubes").input(mask).apply()
+        println("mesh = ${mesh} [${mesh.triangles().size()} triangles, ${mesh.vertices().size()} vertices]")
+
+        meshVolume = ops.op("geom.size").input(mesh).apply().getRealDouble()
+        println("mesh volume = " + meshVolume)
+
+        hull = ops.op("geom.convexHull").input(mesh).apply()
+        println("hull = ${hull} [${hull.triangles().size()} triangles, ${hull.vertices().size()} vertices]")
+
+        hullVolume = ops.op("geom.size").input(hull).apply().getRealDouble()
+        println("hull volume = $hullVolume")
+
+        // Display original image and meshes in 3D Viewer.
+
+        def opsMeshToCustomMesh(opsMesh) {
+            points = []
+            for (t in hull.triangles()) {
+                points.add(new Point3f(t.v0xf(), t.v0yf(), t.v0zf()))
+                points.add(new Point3f(t.v1xf(), t.v1yf(), t.v1zf()))
+                points.add(new Point3f(t.v2xf(), t.v2yf(), t.v2zf()))
+            }
+            return new CustomTriangleMesh(points)
+        }
+
+        mesh_hull = opsMeshToCustomMesh(hull)
+        println("Hull volume according to 3D Viewer: ${mesh_hull.getVolume()}");
+
+        univ = new Image3DUniverse()
+        univ.addVoltex(imp, 1)
+        univ.addCustomMesh(mesh_hull, "Convex Hull")
+        univ.show()
+
+.. _3D Viewer: https://imagej.net/plugins/3d-viewer/
+.. _bat cochlea volume: https://imagej.net/images/bat-cochlea-volume.zip
+.. _bat cochlea info: https://imagej.net/images/bat-cochlea-volume.txt
+.. _marching cubes: https://en.wikipedia.org/wiki/Marching_cubes
diff --git a/docs/ops/doc/index.rst b/docs/ops/doc/index.rst
@@ -33,6 +33,7 @@ The combination of these libraries allows declarative image analysis workflows,
    examples/deconvolution
    examples/flim_analysis
    examples/gaussian_subtraction
+   examples/mesh_viz
    examples/opencv_denoise
    examples/saca
    examples/scyjava