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--- documents:120206pyip_cooking:python_imagej_cookbook [2022/10/03 17:28] – [Threshold to create a mask (Binary)] kota
+++ documents:120206pyip_cooking:python_imagej_cookbook [2022/10/16 07:12] (current) – [Using Java8 Stream] kota
@@ Line 787: / Line 787: @@
 zip command creates a list of tuples from elements of arrays a and b.
+==== Using Java8 Stream ====
+Stream-related syntax introduced from Java8 is useful for writing clear codes, but cannot be directly used in Jython. Below is a way to use StreamAPI, by introducing Jython classes implementing the Consumer interface. I took this idea from [[https://stackoverflow.com/questions/45417732/jython-function-to-java-consumer|here]]. For the Java Stream API, this page is useful: [[https://www.baeldung.com/java-8-streams|The Java 8 Stream API Tutorial]]
+<code python>
+from java.util.Arrays import asList
+from java.util.function import Predicate, Consumer, Function
+from java.util.stream import Collectors
+from java.util import Arrays
+class jc(Consumer):
+    def __init__(self, fn):
+        self.accept=fn
+class jf(Function):
+    def __init__(self, fn):
+        self.apply = fn
+class jp(Predicate):
+    def __init__(self, fn):
+        self.test = fn
+def jprint(x):
+	print x
+tt = ["a", "b", "c"]
+c = Arrays.stream(tt).count()
+print c
+print "forEach printing"
+Arrays.stream(tt).forEach(jc(lambda x: jprint("="+x)))
+print "forEach printing parallelly"
+Arrays.stream(tt).parallel().forEach(jc(lambda x: jprint("="+x)))
+print "has b?", Arrays.stream(tt).anyMatch(jp(lambda x: x=="b"))
+print "has z?", Arrays.stream(tt).anyMatch(jp(lambda x: x=="z"))
+# convert to Java List<>
+jtt = Arrays.asList(tt)
+jtt.stream().forEach(jc(lambda x: jprint("+"+x)))
+</code>
 ===== Event Listener =====
@@ Line 1039: / Line 1083: @@
 === Stack to Mask by a threshold value ===
-Here is an example of using pixel array to binarize an 8-bit stack.
+Here is an example script to create a mask from an 8-bit stack using intensity thresholding.
 The threshold value is derived by the Otsu algorithm using the full stack histogram.
+<code>
+from ij import IJ, ImagePlus, ImageStack
+from ij.plugin import ChannelSplitter
+from ij.process import StackStatistics
+from fiji.threshold import Auto_Threshold
+#imp = IJ.openImage("http://imagej.nih.gov/ij/images/confocal-series.zip")
+imp = IJ.getImage()
+imps = ChannelSplitter.split( imp )
+imp1 = imps[0]
+imp1bin = imp1.duplicate()
+# get auto threshold value
+stats = StackStatistics(imp1bin)
+histdouble = stats.histogram()
+# need this conversion from double to int
+histint = map(lambda x:int(x), histdouble)
+th = Auto_Threshold.Otsu(histint)
+for i in range(imp1bin.getStackSize()):
+	ip = imp1bin.getStack().getProcessor( i + 1)
+	ip.threshold(th)
+IJ.run(imp1bin, "Grays", "");
+imp1bin.show()
+</code>
+To do this by accessing the pixel array of the stack, here is the way. It takes a longer time than above, so this is just to show the technique to process by pixel values using float processor pixel array object.
 <code python>
@@ Line 2307: / Line 2381: @@
 ===== Plugin: MorphoLibJ =====
+Javadoc: [[http://ijpb.github.io/MorphoLibJ/javadoc/]]
 ==== Distance Transform Watershed 3D ====