[wip] Starting to port Albert's scripts

build.gradle.kts

@@ -87,6 +87,9 @@ dependencies {
     implementation("net.imglib2:imglib2")
     implementation("net.imglib2:imglib2-roi")
 
+    // Needed for Albert's tutorials
+    implementation("sc.fiji:legacy-imglib1")
+
     // XDG support
     implementation("dev.dirs:directories:26")
 

src/main/kotlin/sc/iview/ImageJMain.kt

@@ -1,6 +1,8 @@
 package sc.iview
 
+import org.scijava.script.ScriptService
 import org.scijava.ui.UIService
+import java.io.File
 
 object ImageJMain {
     @Throws(Exception::class)
@@ -11,5 +13,11 @@ object ImageJMain {
         val uiService = context?.service(UIService::class.java)
 
         uiService?.showUI()
+
+        val script: String = File("/Users/kharrington/git/scenerygraphics/sciview/src/main/resources/sc/iview/scripts/find_and_count_cells_cardona.py").readText()
+
+        var scriptService = context?.service(ScriptService::class.java) as ScriptService
+
+        scriptService.run("sample.py", script, true)
     }
 }

src/main/resources/sc/iview/scripts/find_and_count_cells_cardona.py

@@ -0,0 +1,102 @@
+#@ SciView sciview
+# Based on https://syn.mrc-lmb.cam.ac.uk/acardona/fiji-tutorial/#find-peaks
+
+# Load an image of the Drosophila larval fly brain and segment  
+# the 5-micron diameter cells present in the red channel.  
+
+from script.imglib.analysis import DoGPeaks  
+from script.imglib.color import Red  
+from script.imglib.algorithm import Scale2D  
+from script.imglib.math import Compute  
+from script.imglib import ImgLib  
+
+from ij import IJ
+from net.imglib2.img.display.imagej import ImageJFunctions
+from org.scijava.util import ColorRGB
+from org.joml import Vector3f
+
+from net.imglib2.img.array import ArrayImgFactory
+
+from net.imglib2 import RandomAccessibleInterval
+from net.imglib2.type.numeric import ARGBType
+from net.imglib2.type.numeric.integer import UnsignedByteType
+from java.util import ArrayList
+
+from graphics.scenery import Sphere
+
+cell_diameter = 5  # in microns  
+minPeak = 40 # The minimum intensity for a peak to be considered so.  
+imp = IJ.openImage("http://samples.fiji.sc/first-instar-brain.zip")  
+
+# Scale the X,Y axis down to isotropy with the Z axis  
+cal = imp.getCalibration()  
+scale2D = cal.pixelWidth / cal.pixelDepth  
+iso = Compute.inFloats(Scale2D(Red(ImgLib.wrap(imp)), scale2D))  
+
+# Find peaks by difference of Gaussian  
+sigma = (cell_diameter  / cal.pixelWidth) * scale2D  
+peaks = DoGPeaks(iso, sigma, sigma * 0.5, minPeak, 1)  
+print("Found", len(peaks), "peaks")
+
+def split_channels(image):
+    channels = ArrayList()
+    num_channels = 4  # Assuming RGBA color model
+
+    for channel_idx in range(num_channels):
+        channel = ArrayImgFactory(UnsignedByteType()).create(image)
+        channel_cursor = channel.cursor()
+        image_cursor = image.cursor()
+
+        while channel_cursor.hasNext():
+            image_cursor.next()
+            alpha = (image_cursor.get().get() >> 24) & 0xFF
+            red = (image_cursor.get().get() >> 16) & 0xFF
+            green = (image_cursor.get().get() >> 8) & 0xFF
+            blue = image_cursor.get().get() & 0xFF
+
+            if channel_idx == 0:
+                channel_cursor.next().setReal(alpha)
+            elif channel_idx == 1:
+                channel_cursor.next().setReal(red)
+            elif channel_idx == 2:
+                channel_cursor.next().setReal(green)
+            elif channel_idx == 3:
+                channel_cursor.next().setReal(blue)                                
+
+        channels.add(channel)
+
+    return channels
+
+# Show the image data
+channels = split_channels(ImageJFunctions.wrap(imp))
+scale = Vector3f([cal.getX(1), cal.getY(1), cal.getZ(1)])
+
+ch1 = sciview.addVolume(channels[1])
+ch1.setScale(Vector3f(scale))
+sciview.setColormap(ch1, "Red.lut")
+
+ch2 = sciview.addVolume(channels[2])
+ch2.setScale(Vector3f(scale))
+sciview.setColormap(ch2, "Green.lut")
+
+ch3 = sciview.addVolume(channels[3])
+ch3.setScale(Vector3f(scale))
+sciview.setColormap(ch3, "Blue.lut")
+
+# Convert the peaks into points in calibrated image space and display
+for peak in peaks:
+    radius = cal.pixelWidth * 1/scale2D
+    node = Sphere(radius, 20)
+    new_pos = Vector3f(peak).mul(Vector3f([1.0/scale2D, 1.0/scale2D, 1.0]))
+    node.spatial().setPosition(new_pos)
+    node.material().setDiffuse(Vector3f(1, 0, 0))
+    ch1.addChild(node)
+    sciview.publishNode(node)
+
+
+ImageJFunctions.show(channels[0])
+ImageJFunctions.show(channels[1])
+ImageJFunctions.show(channels[2])
+ImageJFunctions.show(channels[3])
+
+