Add CellposeStardist template

Adapted from:
https://github.com/imagej/pyimagej/blob/f96da00a37ee7b765c6fd3e94ddd78d14b8e330f/doc/Cellpose-StarDist-Segmentation.ipynb

Author: hinerm

src/main/resources/script_templates/PyImageJ/CellposeStarDistSegmentation.py

@@ -0,0 +1,140 @@
+#@ ImageJ ij
+
+'''
+Note that this script requires a Python environment that includes StarDist and Cellpose
+StarDist currently only supports NumPy 1.x, which necessitates using TensorFlow 2.15 or earlier
+TensorFlow 2.15 itself requires python 3.11 or earlier
+
+You can rebuild your Python environment by using:
+Edit > Options > Python…
+
+The following configuration was used to develop this script:
+
+--Conda dependencies--
+python=3.11
+numpy=1.26.4
+
+--Pip dependencies--
+tensorflow==2.15
+cellpose==4.0.6
+stardist==0.9.0
+csbdeep==0.8.0
+'''
+
+import sys
+import imagej.convert as convert
+import numpy as np
+import matplotlib.pyplot as plt
+from cellpose import models
+from csbdeep.utils import normalize
+from stardist.models import StarDist2D
+import scyjava as sj
+
+def filter_index_image(narr:np.ndarray, min_size:int, max_size:int):
+    """
+    Filter an index image's labels with a pixel size range.
+    """
+    unique = np.unique(narr)
+    for label in unique:
+        if label == 0:
+            # skip the background
+            continue
+        
+        # create a crop for each label
+        bbox = get_bounding_box(np.where(narr == label))
+        bbox_crop = narr[bbox[0]:bbox[2] + 1, bbox[1]:bbox[3] + 1].copy()
+        bbox_crop[bbox_crop != label] = 0
+
+        # get the number of pixels in label
+        bbox_crop = bbox_crop.astype(bool)
+        label_size = np.sum(bbox_crop)
+
+        if not min_size <= label_size <= max_size:
+            narr[narr == label] = 0
+    
+    return narr
+
+def get_bounding_box(indices: np.ndarray):
+    """
+    Get the bounding box coordinates from a the label indices.
+    """
+    # get min and max bounds of indices array
+    min_row = np.min(indices[0])
+    min_col = np.min(indices[1])
+    max_row = np.max(indices[0])
+    max_col = np.max(indices[1])
+
+    return (min_row, min_col, max_row, max_col)
+
+# open image data and convert to Python from Java
+#TODO does this connection need to be closed?
+data = ij.io().open('https://media.imagej.net/pyimagej/3d/hela_a3g.tif')
+xdata = ij.py.from_java(data)
+
+# show the first channel
+ij.ui().show("nucleus", ij.py.to_java(xdata[:, :, 0]))
+
+# show the second channel
+ij.ui().show("cytoplasm", ij.py.to_java(xdata[:, :, 1] * 125))
+
+# run StarDist on nuclei channel
+model = StarDist2D.from_pretrained('2D_versatile_fluo')
+nuc_labels, _ = model.predict_instances(normalize(xdata[:, :, 0]))
+
+# run Cellpose on cytoplasm (grayscale)
+model = models.CellposeModel(gpu=False, model_type='cyto')
+ch = [0, 0]
+cyto_labels = model.eval(xdata[:, :, 1].data, channels=ch, diameter=72.1)
+
+# show the stardist results
+ij.ui().show("StarDist results", ij.py.to_java(nuc_labels))
+ij.IJ.run("mpl-viridis", "");
+
+# show the second channel
+ij.ui().show("Cellpose results", ij.py.to_java(cyto_labels[0]))
+ij.IJ.run("mpl-viridis", "");
+
+# filter the stardist results and display
+filter_index_image(nuc_labels, 500, 10000)
+ij.ui().show("StarDist filtered", ij.py.to_java(nuc_labels))
+ij.IJ.run("mpl-viridis", "");
+
+# ensure ROI Manager exists
+rm = ij.RoiManager.getInstance()
+if rm is None:
+    ij.IJ.run("ROI Manager...")
+    rm = ij.RoiManager.getInstance()
+
+# Reset the ROI manager
+rm.reset()
+
+# convert to ImgLib2 ROI in a ROITree
+nuc_roi_tree = convert.index_img_to_roi_tree(ij, nuc_labels)
+cyto_roi_tree = convert.index_img_to_roi_tree(ij, cyto_labels[0])
+
+# print the contents of the ROITree (nuclear ROIs)
+len(nuc_roi_tree.children())
+for i in range(len(nuc_roi_tree.children())):
+    print(nuc_roi_tree.children().get(i).data())
+
+# display the input data, select channel 2 and enhance the contrast
+data_title = "hela_a3g.tif"
+ij.ui().show(data_title, data)
+imp = ij.WindowManager.getImage(data_title)
+imp.setC(2)
+ij.IJ.run(imp, "Enhance Contrast", "saturated=0.35")
+
+# convert a single ImgLib2 roi to a legacy ImageJ ROI with the ConvertService.
+imglib_polygon_roi = nuc_roi_tree.children().get(0).data()
+ij_polygon_roi = ij.convert().convert(imglib_polygon_roi, sj.jimport('ij.gui.PolygonRoi'))
+print(type(ij_polygon_roi))
+
+# convert index images to ImageJ ROI in RoiManager
+#TODO any way to color the selections? We can use Colors... but it appears to be global and the last one run wins
+#ij.IJ.run(imp, "Colors...", "foreground=blue background=black selection=red");
+convert.index_img_to_roi_manager(ij, nuc_labels)
+convert.index_img_to_roi_manager(ij, cyto_labels[0])
+
+#TODO this pops an unnecessary display at the end but if I don't make it the last line the ROIs don't show
+rm.moveRoisToOverlay(imp)
+rm.runCommand(imp, "Show All")