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A Fiji Scripting Tutorial

Most of what you want to do with an image exists in Fiji.
What happens is: you still don't know what it's called, and where it is.

This tutorial will provide you with the general idea of how Fiji works: how are its capabilities organized, and how can they be composed into a program.

To learn about Fiji, we'll start the hard way: by programming.
Your first program will be very simple: obtain an image, and print out its title. We'll slowly iterate towards increasingly complex programs.

This tutorial will teach you both python and Fiji.


Index
  1. Getting started
  2. Your first Fiji script
  3. Inspecting properties and pixels of an image
  4. Running ImageJ / Fiji plugins on an ImagePlus
  5. Creating images and regions of interest (ROIs)
  6. Create and manipulate image stacks
  7. Interacting with humans: file and option dialogs, messages, progress bars.
  8. Turn your script into a plugin
  9. Lists, native arrays, and passing lists and arrays to Java classes
  10. Generic algorithms that work on images of any kind: using Imglib
  11. ImgLib2: writing generic, high-performance image processing programs

Tutorial created by Albert Cardona. Zurich, 2010-11-10.
(Last update: 2018-08-11)

All source code is under the Public Domain.

Remember: Fiji is just ImageJ (batteries included).


See also:

Thanks to:

  • 2018-07-22: Nikolas Schnellbächer for reporting an error in a script.

1. Getting started

Open the Script Editor by choosing "File - New - Script".

Alternatively, use the Command finder:

Push 'l' (letter L) and then start typing "scri".
You will see a list of Fiji commands, getting shorter the more letters you type. When the "Script Editor" command is visible, push the up arrow to go to it, and then push return to launch it.
(Or double-click on it.)

The Command Finder is useful for invoking any Fiji command.

2. Your first Fiji script

We'll need an image to work on: please open any image.
For example, go to "File - Open Samples - Boats (356K)".

This tutorial will use the programming language Python 2.5. We start by telling the opened Script Editor what language you want to write the script on: choose "Language - Python".

Grabbing an open image

Type in what you see on the image to the right into the Script Editor, and then push "Run", or choose "Run - Run", or control+R (command+R in MacOSX).
The program will execute and print, at the bottom, its result.

Line by line:

  1. Import the namespace "IJ" from the package "ij".
    A namespace is a group of functions. And a package is a group of namespaces.
    Just imagine: if all functions were in the same namespace, it would be huge, and you wouldn't be able to have repeated names. Organizing functions in small namespaces is a great idea.
  2. (An empty line)
  3. Assign the result of invoking the function "getImage" from the namespace "IJ" to the local variable "imp".
    So now "imp" points to the last image you opened, or whose window was brought to focus by a mouse click. In our example, it's the "boats" image.
  4. Print the contents of the variable "imp".
    Notice how, at the bottom, the script first printed its own title "New_.py" and the starting time, and then printed "imp[boats.gif 720x576x1]"--which is just some data on the boats image: the title "boats.gif" and the dimensions of the image, in pixels.

So what is "imp"? "imp" is a commonly used name to refer to an instance of an ImagePlus. The ImagePlus is one of ImageJ's abstractions to represent an image. Every image that you open in ImageJ is an instance of ImagePlus.


Saving an image with a file dialog

The first action we'll do on our image is to save it.

To do that, you could call "File - Save" from the menus.
In our program, we import the namespace "FileSaver" and then create a new instance of FileSaver with our image "imp" as the only parameter. Then we invoke the function "save" on it, which will open a file dialog. After choosing a name and a folder, the image will be saved in TIFF format.

Saving an image directly to a file

The point of running a script is to avoid human interaction.
We want to save an image automatically: we tell the FileSaver instance where it should save our image, and in what format (like TIFF with saveAsTiff). The FileSaver offers more methods, such as saveAsPng, saveAsJpeg, etc.

Notice that the '#' sign defines comments. In python, any text after a '#' is not executed.

from ij import IJ
from ij.io import FileSaver

imp = IJ.getImage()
fs = FileSaver(imp)

# A known folder to store the image at:
folder = "/home/albert/Desktop/t2/fiji-tutorial"

filepath = folder + "/" + "boats.tif"
fs.saveAsTiff(filepath):
          

Saving an image ... checking first if it's a good idea.

The FileSaver will overwrite whatever file exists at the file path that you give it. That is not always a good idea!

Here, we write the same code but checking first:

  1. If the folder exists at all, and whether the file at that file path is really a folder.
  2. If a file with the same name as the file we are about to write is already there--to avoid overwriting, if desired.
  3. If the FileSaver.saveAsTiff call really worked, or failed.
    Notice in the documentation for FileSaver.saveAsTiff that this method returns a boolean variable: it will be true if all went well, and false if the image could not be saved in the file.

And finally, if all expected preconditions hold, then we place the call to saveAsTiff.


This script introduced three new programming items:

  • if, else, and elif ("elif" being a combination of "else" and "if")
  • The concept of a code block, which, in python, starts with a ':' and then the code lines are indented.
    Notice how the code below the if or the else are indented to the right. By how much, it doesn't matter, as long as it's consistent.
  • The os.path namespace, which contains utility functions for inspecting files and folders (also called "directories").
from ij import IJ
from ij.io import FileSaver
from os import path

imp = IJ.getImage()
fs = FileSaver(imp)

# A known folder to store the image at:
folder = "/home/albert/Desktop/t2/fiji-tutorial"

# Test if the folder exists before attempting to save the image:
if path.exists(folder) and path.isdir(folder):
  print "folder exists:", folder
  filepath = path.join(folder, "boats.tif") # Operating System-specific
  if path.exists(filepath):
    print "File exists! Not saving the image, would overwrite a file!"
  elif fs.saveAsTiff(filepath):
    print "File saved successfully at ", filepath
else:
  print "Folder does not exist or it's not a folder!"
          

3. Inspecting properties and pixels of an image

An image in ImageJ or Fiji is, internally, an instance of ImagePlus.
The ImagePlus contains data such as the title and dimensions of the image (width, height, number of slices, number of time frames, number of channels), as well as the pixels, which are wrapped in an ImageProcessor instance.
Each of these data is stored internally in a field of the ImagePlus class. The field is nothing else than a variable, which, for a given image instance, points to a specific value.
For example, the "title" field points to "boats.gif" for the instance of ImagePlus that contains the sample boats image that we opened earlier.

In python, accessing fields of an instance is straightforward: just add a dot '.' between the variable "imp" and the field "title" to access.

In the Fiji API documentation, if you don't see a specific field like width in a particular class, but there is a getWidth method, then from python they are one and the same.

The image type

Notice how we created a dictionary to hold key/value pairs: of the image type versus a text representation of that type. This dictionary (also called map or table in other programming languages) then lets us ask it for a specific image type (such as ImagePlus.GRAY8), and we get back the corresponding text, such as "8-bit".

You may have realized by now that the ImagePlus.getType() (or what is the same in python: "imp.type") returns us any of the controled values of image type that an ImagePlus instance can take. These values are GRAY8, GRAY16, GRAY32, COLOR_RGB, and COLOR_256.

What is the image type? It's the kind of pixel data that the image holds. It could be numbers from 0 to 255 (what fits in an 8-bit range), or from 0 to 65536 (values that fit in a 16-bit range), or could be three channels of 8-bit values (an RGB image), or floating-point values (32-bit).

The COLOR_256 indicates an 8-bit image that has an associated look-up table: each pixel value does not represent an intensity, but rather it's associated with a color. The table of values versus colors is limited to 256, and hence these images may not look very well. For image processing, you should avoid COLOR_256 images (also known as "8-bit color" images). These images are meant for display in the web in ".gif" format, but have been superseeded by JPEG or PNG.

The GRAY_8 ("8-bit"), GRAY_16 ("16-bit") and GRAY_32 ("32-bit") images may also be associated with a look-up table. For example, in a "green" look-up table on an 8-bit image, values of zero are black, values of 128 are darkish green, and the maximum value of 255 is fully pure green.

from ij import IJ, ImagePlus

# Grab the last activated image
imp = IJ.getImage()

# Print image details
print "title:", imp.title
print "width:", imp.width
print "height:", imp.height
print "number of pixels:", imp.width * imp.height
print "number of slices:", imp.getNSlices()
print "number of channels:", imp.getNChannels()
print "number of time frames:", imp.getNFrames()

types = {ImagePlus.COLOR_RGB : "RGB",
         ImagePlus.GRAY8 : "8-bit",
         ImagePlus.GRAY16 : "16-bit",
         ImagePlus.GRAY32 : "32-bit",
         ImagePlus.COLOR_256 : "8-bit color"}

print "image type:", types[imp.type]
          
Started New_.py at Wed Nov 10 14:57:46 CET 2010
title: boats.gif
width: 720
height: 576
number of pixels: 414720
number of slices: 1
number of channels: 1
number of time frames: 1
image type: 8-bit
          

Obtaining pixel statistics of an image (and your first function)

ImageJ / Fiji offers an ImageStatistics class that does all the work for us.
The ImageStatistics class offers a convenient getStatistics static method. (A static method is a function, in this case of the ImageStatistics namespace, that is unrelated to a class instance. Java confuses namespaces with class names).

Notice how we import the ImageStatistics namespace as "IS", i.e. we alias it--it's too long to type!

The options variable is the bitwise-or combination of three different static fields of the ImageStatistics class. The final options is an integer that has specific bits set that indicate mean, median and min and max values.
(Remember that in a computer, an integer number is a set of bits, such as 00001001. In this example, we'd say that the first and the fourth bits are set. Interpreting this sequence of 0 and 1 in binary gives the integer number 4097 in decimal).

from ij import IJ
from ij.process import ImageStatistics as IS

# Grab the active image
imp = IJ.getImage()

# Get its ImageProcessor
ip = imp.getProcessor()

options = IS.MEAN | IS.MEDIAN | IS.MIN_MAX
stats = IS.getStatistics(ip, options, imp.getCalibration())

# print statistics on the image
print "Image statistics for", imp.title
print "Mean:", stats.mean
print "Median:", stats.median
print "Min and max:", stats.min, "-", stats.max
          
Started New_.py at Wed Nov 10 19:54:37 CET 2010
Image statistics for boats.gif
Mean: 120.026837384
Median: 138.0
Min and max: 3.0 - 220.0
          

Now, how about obtaining statistics for a lot of images?
From a list of images in a folder, we would have to:

  1. Load each image
  2. Get statistics for it

So we define a folder that contains our images, and we loop the list of filenames that it has. For every filename that ends with ".tif", we load it as an ImagePlus, and handle it to the getStatistics function, which returns us the mean, median, and min and max values.

(Note: if the images are stacks, use StackStatistics instead.)

This script introduces a few new concepts:

  • Defining a function: it's done with def, followed by the desired function name, and any number of comma-separated arguments between parenthesis. The function is a code block--remember the code block is specified with indentation (any amount of indentation, as long as it's consistent).
  • The triple quote """ : defines a string of text over multiple lines. It's also the convention for adding documentation to a function in python.
  • The global keyword: lets you read, from within a function code block, a variable defined outside of the function code block.
  • The for loop: to iterate every element of a list. In this case, every filename in the list of filenames of a folder, which we obtain from the os.listdir function.
    Notice the continue keyword, used to jump to the next loop iteration when desired. In the example, when the image couldn't be loaded.

See also the python documentation page on control flow, with explanations on the keywords if, else and elif, the for loop keyword and the break and continue keywords, defining a function with def, functions with variable number of arguments, anonymous functions (with the keyword lambda), and guidelines on coding style.


 
from ij import IJ
from ij.process import ImageStatistics as IS
import os

options = IS.MEAN | IS.MEDIAN | IS.MIN_MAX

def getStatistics(imp):
  """ Return statistics for the given ImagePlus """
  global options
  ip = imp.getProcessor()
  stats = IS.getStatistics(ip, options, imp.getCalibration())
  return stats.mean, stats.median, stats.min, stats.max


# Folder to read all images from:
folder = "/home/albert/Desktop/t2/fiji-tutorial"

# Get statistics for each image in the folder
# whose file extension is ".tif":
for filename in os.listdir(folder):
  if filename.endswith(".tif"):
    print "Processing", filename
    imp = IJ.openImage(os.path.join(folder, filename))
    if imp is None:
      print "Could not open image from file:", filename
      continue
    mean, median, min, max = getStatistics(imp)
    print "Image statistics for", imp.title
    print "Mean:", mean
    print "Median:", median
    print "Min and max:", min, "-", max
  else:
    print "Ignoring", filename
          

Iterating pixels

Iterating pixels is considered a low-level operation that you would seldom, if ever, have to do. But just so you can do it when you need to, here are various ways to iterate all pixels in an image.

The three iteration methods:

  1. The C-style method, where we iterate over a list of numbers from zero to length of the pixel array minus one, and obtain each pixel by doing an array lookup.
    The list of numbers is obtained by calling the built-in function xrange, which delivers a lazy sequence of 0, 1, 2, ... up to the length of the pixel array minus one.
    The length of the pixels array is obtained by calling the built-in function len.
  2. The iterator method, where the pixels array is iterated as if it was a list, and the pix variable takes the value of each pixel.
  3. The functional method, were instead of looping, we reduce the array to a single value (the minimum) by applying the min function to every adjacent pair of pixel values in the pixels array. (Realize that any function that takes two arguments, like min, could have been used with reduce.)

The last should be your preferred method. There's the least opportunity for introducting an error, and it is very concise.


Regarding the example given, keep in mind:

  • That the pixels variable points to an array of pixels, which can be any of byte[], short[], float[], or int[] (for RGB images, with the 3 color channels channels bit-packed).
  • That the example method for finding out the minimum value would NOT work for RGB images, because they have the three 8-bit color channels packed into a single integer value.
    For an RGB image, you'd want to ask which pixel is the least bright. It's easy to do so by calling getBrightness() on the ImageProcessor of an RGB image (which is a ColorProcessor). Or compute the minimum for one of its color channels, which you get with the method ip.toFloat(0, None) to get the red channel (1 is green, and 2 is blue).

  •  
from ij import IJ

# Grab the active image
imp = IJ.getImage()

# Grab the image processor converted to float values
# to avoid problems with bytes
ip = imp.getProcessor().convertToFloat() # as a copy
# The pixels points to an array of floats
pixels = ip.getPixels()

print "Image is", imp.title, "of type", imp.type

# Obtain the minimum pixel value

# Method 1: the for loop, C style
minimum = Float.MAX_VALUE
for i in xrange(len(pixels)):
  if pixels[i] < minimum:
    minimum = pixels[i]

print "1. Minimum is:", minimum

# Method 2: iterate pixels as a list
minimum = Float.MAX_VALUE
for pix in pixels:
  if pix < minimum:
    minimum = pix

print "2. Minimum is:", minimum

# Method 3: apply the built-in min function
# to the first pair of pixels,
# and then to the result of that and the next pixel, etc.
minimum = reduce(min, pixels)

print "3. Minimum is:", minimum
         
Started New_.py at Wed Nov 10 20:49:31 CET 2010
Image is boats.gif of type 0
1. Minimum is: 3.0
2. Minimum is: 3.0
3. Minimum is: 3.0
          

On iterating or looping lists or collections of elements

Ultimately all operations that involve iterating a list or a collection of elements can be done with the for looping construct. But in almost all occasions the for is not the best choice, neither regarding performance nor in clarity or conciseness. The latter is important to minimize the amount of errors that we may possibly introduce without noticing.

There are three kinds of operations to perform on lists or collections: map, reduce, and filter. We show them here along with the equivalent for loop.


 

A map operation takes a list of length N and returns another list also of length N, with the results of applying a function (that takes a single argument) to every element of the original list.

For example, suppose you want to get a list of all images open in Fiji.

With the for loop, we have to create first a list explictly and then append one by one every image.

With list comprehension, the list is created directly and the logic of what goes in it is placed inside the square brackets--but it is still a loop. That is, it is still a sequential, unparallelizable operation.

With the map, we obtain the list automatically by executing the function WM.getImage to every ID in the list of IDs.

While this is a trivial example, suppose you were executing a complex operation on every element of a list or an array. If you were to redefine the map function to work in parallel, suddenly any map operation in your program will run faster, without you having to modify a single line of tested code!


 
from ij import WindowManager as WM
            
# Method 1: with a 'for' loop
images = []
for id in WM.getIDList():
  images.append(WM.getImage(id))

# Method 2: with list comprehension
images = [WM.getImage(id) for id in WM.getIDList()]

# Method 3: with a 'map' operation
images = map(WM.getImage, WM.getIDList())

          

A filter operation takes a list of length N and returns a shorter list, with anywhere from 0 to N elements. Only those elements of the original list that pass a test are placed in the new, returned list.

For example, suppose you want to find the subset of opened images in Fiji whose title match a specific criterium.

With the for loop, we have to create a new list first, and then append elements to that list as we iterate the list of images.

The second variant of the for loop uses list comprehension. The code is reduced to a single short line, which is readable, but is still a python loop (with potentially lower performance).

With the filter operation, we get the (potentially) shorter list automatically. The code is a single short line, instead of 4 lines!


 
from ij import WindowManager as WM

# A list of all open images
imps = map(WM.getImage, WM.getIDList())

def match(imp):
  """ Returns true if the image title contains the word 'cochlea'"""
  return imp.title.find("cochlea") > -1

# Method 1: with a 'for' loop
# (We have to explicitly create a new list)
matching = []
for imp in imps:
  if match(imp):
    matching.append(imp)

# Method 2: with list comprehension
matching = [imp for imp in imps if match(imp)]

# Method 3: with a 'filter' operation
matching = filter(match, imps)
          

A reduce operation takes a list of length N and returns a single value. This value is composed from applying a function that takes two arguments to the first two elements of the list, then to the result of that and the next element, etc. Optionally an initial value may be provided, so that the cycle starts with that value and the first element of the list.

For example, suppose you want to find the largest image, by area, from the list of all opened images in Fiji.

With the for loop, we have to we have to keep track of which was the largest area in a pair of temporary variables. And even check whether the stored largest image is null! We could have initizalized the largestArea variable to the first element of the list, and then start looping at the second element by slicing the first element off the list (with "for imp in imps[1:]:"), but then we would have had to check if the list contains at least one element.

With the reduce operation, we don't need any temporary variables. All we need is to define a helper function (which could have been an anonymous lambda function, but we defined it explicitly for extra clarity and reusability).


 
from ij import IJ

from ij import WindowManager as WM

# A list of all open images
imps = map(WM.getImage, WM.getIDList())

def area(imp):
  return imp.width * imp.height

# Method 1: with a 'for' loop
largest = None
largestArea = 0
for imp in imps:
  if largest is None:
    largest = imp
  else:
    a = area(imp)
    if a > largestArea:
      largest = imp
      largestArea = a

# Method 2: with a 'reduce' operation
def largestImage(imp1, imp2):
  return imp1 if area(imp1) > area(imp2) else imp2

largest = reduce(largestImage, imps)
          

Subtract the min value to every pixel

First we obtain the minimum pixel value, using the reduce method explained just above.

Then we subtract this minimum value to every pixel. We have two ways to do it:

  1. In place, by iterating the pixel array C-style and setting a new value to each pixel: that of itself minus the minimum value.
  2. On a new list: we declare an anonymous function (with lambda instead of def) that takes one argument x (the pixel value), subtracts the minimum from it, and returns the result. We map (in other words, we apply) this function to every pixel in the pixels array, returning a new list of pixels with the results.

With the first method, since the pixels array was already a copy (notice we called convertToFloat() on the ImageProcessor), we can use it to create a new ImagePlus with it without any unintended consequences.

With the second method, the new list of pixels must be given to a new FloatProcessor instance, and with it, a new ImagePlus is created, of the same dimensions as the original.

from ij import IJ

imp = IJ.getImage()
ip = imp.getProcessor().convertToFloat() # as a copy
pixels = ip.getPixels()

# Apply the built-in min function
# to the first pair of pixels,
# and then to the result of that and the next pixel, etc.
minimum = reduce(min, pixels)

# Method 1: subtract the minim from every pixel,
# in place, modifying the pixels array
for i in xrange(len(pixels)):
  pixels[i] -= minimum
# ... and create a new image:
imp2 = ImagePlus(imp.title, ip)

# Method 2: subtract the minimum from every pixel
# and store the result in a new array
pixels3 = map(lambda x: x - minimum, pixels)
# ... and create a new image:
ip3 = FloatProcessor(ip.width, ip.height, pixels3, None)
imp3 = ImagePlus(imp.title, ip3)

# Show the images in an ImageWindow:
imp2.show()
imp3.show()
          

Reduce a pixel array to a single value: count pixels above a threshold

Suppose you want to analyze a subset of pixels. For example, find out how many pixels have a value over a certain threshold.

The reduce built-in function is made just for that. It takes a function with two arguments (the running count and the next pixel); the list or array of pixels; and an initial value (in this case, zero) for the first argument (the "count'), and will return a single value (the total count).

In this example, we computed first the mean pixel intensity, and then filtered all pixels for those whose value is above the mean. Notice that we compute the mean by using the convenient built-in function sum, which is able to add all numbers contained in any kind of collection (be it a list, a native array, a set of unique elements, or the keys of a dictionary). We could imitate the built-in sum function with reduce(lambda s, x: s + x, pixels), but paying a price in performance.

Notice we are using anonymous functions again (that is, functions that lack a name), declared in place with the lambda keyword. A function defined with def would do just fine as well.


from ij import IJ

# Grab currently active image
imp = IJ.getImage()
ip = imp.getProcessor().convertToFloat()
pixels = ip.getPixels()

# Compute the mean value (sum of all divided by number of pixels)
mean = sum(pixels) / len(pixels)

# Count the number of pixels above the mean
n_pix_above = reduce(lambda count, a: count + 1 if a > mean else count, pixels, 0)

print "Mean value", mean
print "% pixels above mean:", n_pix_above / float(len(pixels)) * 100
          
Started New_.py at Thu Nov 11 01:50:49 CET 2010
Mean value 120.233899981
% pixels above mean: 66.4093846451
          

Another useful application of filtering pixels by their value: finding the coordinates of all pixels above a certain value (in this case, the mean), and then calculating their center of mass.

The filter built-in function is made just for that. The indices of the pixels whose value is above the mean are collected in a list named "above", which is created by filtering the indices of all pixels (provided by the built-in function xrange). The filtering is done by an anonymous function declared with lambda, with a single argument: the index i of the pixel.

Here, note that in ImageJ, the pixels of an image are stored in a linear array. The length of the array is width * height, and the pixels are stored as concatenated rows. Therefore, the modulus of dividing the index of a pixel by the width the image provides the X coordinate of a pixel. Similarly, the integer division of the index of a pixel by the width provides the Y coordinate.

To compute the center of mass, there are two equivalent methods. The C-style method with a for loop, with every variable being declared prior to the loop, and then modified at each loop iteration and, after the loop, dividing the sum of coordinates by the number of coordinates (the length of the "above" list). For this example, this is the method with the best performance.

The second method computes the X and Y coordinates of the center of mass with a single line of code for each. Notice that both lines are nearly identical, differing only in the body of the function mapped to the "above" list containing the indices of the pixels whose value is above the mean. While, in this case, the method is less performant due to repeated iteration of the list "above", the code is shorter, easier to read, and with far less opportunities for introducing errors. If the actual computation was far more expensive than the simple calculation of the coordinates of a pixel given its index in the array of pixels, this method would pay off for its clarity.


from ij import IJ

# Grab the currently active image
imp = IJ.getImage()
ip = imp.getProcessor().convertToFloat()
pixels = ip.getPixels()

# Compute the mean value
mean = sum(pixels) / len(pixels)

# Obtain the list of indices of pixels whose value is above the mean
above = filter(lambda i: pixels[i] > mean, xrange(len(pixels)))

print "Number of pixels above mean value:", len(above)

# Measure the center of mass of all pixels above the mean

# The width of the image, necessary for computing the x,y coordinate of each pixel
width = imp.width

# Method 1: with a for loop
xc = 0
yc = 0
for i in above:
  xc += i % width # the X coordinate of pixel at index i
  yc += i / width # the Y coordinate of pixel at index i
xc = xc / len(above)
yc = yc / len(above)
print xc, yc

# Method 2: with sum and map
xc = sum(map(lambda i: i % width, above)) / len(above)
yc = sum(map(lambda i: i / width, above)) / len(above)
print xc, yc
          

The third method pushes the functional approach too far. While written in a single line, that doesn't mean it is clearer to read: it's intent is obfuscated by starting from the end: the list comprehension starts off by stating that each element (there are only two) of the list resulting from the reduce has to be divided by the length of the list of pixels "above", and only then we learn than the collection being iterated is the array of two coordinates, created at every iteration over the list "above", containing the sum of all coordinates for X and for Y. Notice that the reduce is invoked with three arguments, the third one being the list [0, 0] containing the initialization values of the sums. Confusing! Avoid writing code like this. Notice as well that, by creating a new list at every iteration step, this method is the least performant of all.

The fourth method is a clean up of the third method. Notice that we import the partial function from the functools package. With it, we are able to create a version of the "accum" helper function that has a frozen "width" argument (also known as currying a function). In this way, the "accum" function is seen by the reduce as a two-argument function (which is what reduce needs here). While we regain the performance of the for loop, notice that now the code is just as long as with the for loop. The purpose of writing this example is to illustrate how one can write python code that doesn't use temporary variables, these generally being potential points of error in a computer program. It is always better to write lots of small functions that are easy to read, easy to test, free of side effects, documented, and that then can be used to assemble our program.

# (Continues from above...)

# Method 3: iterating the list "above" just once
xc, yc = [d / len(above) for d in
            reduce(lambda c, i: [c[0] + i % width, c[1] + i / width], above, [0, 0])]
print xc, yc

# Method 4: iterating the list "above" just once, more clearly and performant
from functools import partial

def accum(width, c, i):
  """ Accumulate the sum of the X,Y coordinates of index i in the list c."""
  c[0] += i % width
  c[1] += i / width
  return c

xy, yc = [d / len(above) for d in reduce(partial(accum, width), above, [0, 0])]
print xc, yc
          


4. Running ImageJ / Fiji plugins on an ImagePlus

Here is an example plugin run programmatically: a median filter applied to the currently active image.

The median filter, along with the mean, minimum, maximum, variance, remove outliers and despeckle menu commands, are implemented in the RankFilters class.
A new instance of RankFilters is created (notice the "()" after "RankFilters"), and we call its method rank with the ImageProcessor, the radius, and the desired filter flag as arguments.
With the result, we create a new ImagePlus and we show it.

from ij import IJ
from ij.plugin.filter import RankFilters

# Grab the active image
imp = IJ.getImage()
ip = imp.getProcessor().convertToFloat() # as a copy

# Remove noise by running a median filter
# with a radius of 2
radius = 2
RankFilters().rank(ip, radius, RankFilters.MEDIAN)

imp2 = ImagePlus(imp.title + " median filtered", ip)
imp2.show()
          

Finding the class that implements a specific ImageJ command

When starting ImageJ/Fiji programming, the problem is not so much how to run a plugin on an image, as it is to find out which class implements which plugin. Here is a simple method to find out, via the Command Finder:

  1. Open the Command Finder by pushing 'l' or going to "Plugins - Utilities - Find commands...".
  2. Type "FFT". A bunch of FFT-related commands are listed.
  3. Click on the "Show full information" checkbox at the bottom.
  4. Read, next to each listed command, the plugin class that implements it.

Notice that the plugin class comes with some text. For example:

FFT (in Process > FFT) [ij.plugin.FFT("fft")]
Inverse FFT (in Process > FFT) [ij.plugin.FFT("inverse")]
          

The above two commands are implemented by a single plugin (ij.plugin.FFT) whose run method accepts, like all PlugIn, a text string specifying the action: the fft, or the inverse.
The first part of the information shows where in the menus you will find the command. In this case, under menu "Process", submenu "FFT".


Finding the java documentation for any class

Once you have found the PlugIn class that implements a specific command, you may want to use that class directly. The information is either in the online java documentation or in the source code. How to find these?

  • The Fiji java documentation can be opened directly from the Script Editor for a specific class. Type in the name of the class, select it, and then execute the menu "Tools - Open help for class (with frames)". A new web browser window will open, with the web page corresponding to the class in question. When there is more than one possible class (because they share the same name but live in different packages), then a dialog will prompt for choosing the correct one.
  • The source code for a plugin included in Fiji is in the Fiji git repository. The fastest way to find the corresponding java class is to Google it. Of course another way to search is directly in the Fiji source code repository, which has a search box to look up the source code of a plugin by its name their own repositories. The ImageJ source code is perhaps the easiest to browse, but contains only the core ImageJ library source code.

Figuring out what parameters a plugin requires

To do that, we'll use the Macro Recorder. Make sure that an image is open. Then:

  1. Open the "Plugins - Macros - Record..."
  2. Run the command of your choice, such as "Process - Filters - Median..."
    A dialog opens. Set the desired radius, and push "OK".
  3. Look into the Recorder window:
    run("Median...", "radius=2");
                

That is valid macro code, that ImageJ can execute. The first part is the command ("Median..."), the second part is the parameters that that command uses; in this case, just one ("radius=2"). If there were more parameters, they would be separated by spaces.


Running a command on an image

We can use these macro recordings to create jython code that executes a given plugin on a given image. Here is an example.

Very simple! The IJ namespace has a function, run, that accepts an ImagePlus as first argument, then the name of the command to run, and then the macro-ready list of arguments that the command requires.
When executing this script, no dialogs are shown!
Behind the curtains, ImageJ is placing the right parameters in the right places, making it all just work.


from ij import IJ

# Grab the active image
imp = IJ.getImage()

# Run the median filter on it, with a radius of 2
IJ.run(imp, "Median...", "radius=2")
          

5. Creating images and regions of interest (ROIs)

Create an image from scratch

An ImageJ/Fiji image is composed of at least three objects:

  • The pixels array: an array of primitive values.
    (Where primitive is one of byte, short, int, or float.)
  • The ImageProcessor subclass instance that holds the pixels array.
  • The ImagePlus instance that holds the ImageProcessor instance.

In the example, we create an empty array of floats (see creating native arrays), and fill it in with random float values. Then we give it to a FloatProcessor instance, which is then wrapped by an ImagePlus instance. Voilà!

from ij import ImagePlus
from ij.process import FloatProcessor
from array import zeros
from random import random

width = 1024
height = 1024
pixels = zeros('f', width * height)

for i in xrange(len(pixels)):
  pixels[i] = random()

fp = FloatProcessor(width, height, pixels, None)
imp = ImagePlus("White noise", fp)

imp.show()
          

Fill a region of interest (ROI) with a given value

To fill a region of interest in an image, we could iterate the pixels, find the pixels that lay within the bounds of interest, and set their values to a specified value. But that tedious and error prone. Much more effective is to create an instance of a Roi class or one of its subclasses (PolygonRoi, OvalRoi, ShapeRoi, etc.) and tell the ImageProcessor to fill that region.

In this example, we create an image filled with white noise like before, and then define a rectangular region of interest in it, which is filled with a value of 2.0.

The white noise is drawn from a random distribution whose values range from 0 to 1. When filling an area of the FloatProcessor with a value of 2.0, that is the new maximum value. The area with 2.0 pixel values will look white (look at the status bar):


from ij import IJ, ImagePlus
from ij.process import FloatProcessor
from array import zeros
from random import random
from ij.gui import Roi, PolygonRoi

# Create a new ImagePlus filled with noise
width = 1024
height = 1024
pixels = zeros('f', width * height)

for i in xrange(len(pixels)):
  pixels[i] = random()

fp = FloatProcessor(width, height, pixels, None)
imp = ImagePlus("Random", fp)

# Fill a rectangular region of interest
# with a value of 2:
roi = Roi(400, 200, 400, 300)
fp.setRoi(roi)
fp.setValue(2.0)
fp.fill()

# Fill a polygonal region of interest
# with a value of -3
xs = [234, 174, 162, 102, 120, 123, 153, 177, 171,
      60, 0, 18, 63, 132, 84, 129, 69, 174, 150,
      183, 207, 198, 303, 231, 258, 234, 276, 327,
      378, 312, 228, 225, 246, 282, 261, 252]
ys = [48, 0, 60, 18, 78, 156, 201, 213, 270, 279,
      336, 405, 345, 348, 483, 615, 654, 639, 495,
      444, 480, 648, 651, 609, 456, 327, 330, 432,
      408, 273, 273, 204, 189, 126, 57, 6]
proi = PolygonRoi(xs, ys, len(xs), Roi.POLYGON)
fp.setRoi(proi)
fp.setValue(-3)
fp.fill(proi.getMask())  # Attention!

imp.show()
          

6. Create and manipulate image stacks and hyperstacks

Load a color image stack and extract its green channel

First we load the stack from the web--it's the "Fly Brain" sample image.

Then we iterate its slices. Each slice is a ColorProcessor: wraps an integer array. Each integer is represented by 4 bytes, and the lower 3 bytes represent, respectively, the intensity values for red, green and blue. The upper most byte is usually reserved for alpha (the inverse of transparency), but ImageJ ignores it.

Dealing with low-level details like that is not necessary. The ColorProcessor has a method, toFloat, that can give us a FloatProcessor for a specific color channel. Red is 0, green is 1, and blue is 2.

Representing the color channel in floats is most convenient for further processing of the pixel values--won't overflow like a byte would.

In this example, all we do is collect each slice into a list of slices we named greens. Then we add all the slices to a new ImageStack, and pass it to a new ImagePlus. Then we invoke the "Green" command on that ImagePlus instance, so that a linear green look-up table is assigned to it. And we show it.

from ij import IJ, ImagePlus, ImageStack

# Load a stack of images: a fly brain, in RGB
imp = IJ.openImage("https://imagej.nih.gov/ij/images/flybrain.zip")
stack = imp.getImageStack()

print "number of slices:", imp.getNSlices()

# A list of green slices
greens = []

# Iterate each slice in the stack
for i in xrange(1, imp.getNSlices()+1):
  # Get the ColorProcessor slice at index i
  cp = stack.getProcessor(i)
  # Get its green channel as a FloatProcessor
  fp = cp.toFloat(1, None)
  # ... and store it in a list
  greens.append(fp)

# Create a new stack with only the green channel
stack2 = ImageStack(imp.width, imp.height)
for fp in greens:
  stack2.addSlice(None, fp)

# Create a new image with the stack of green channel slices
imp2 = ImagePlus("Green channel", stack2)
# Set a green look-up table:
IJ.run(imp2, "Green", "")
imp2.show()
          

Convert an RGB stack to a 2-channel, 32-bit hyperstack

We load an RGB stack--the "Fly brain" sample image, as before.

Suppose we want to analyze each color channel independently: an RGB image doesn't really let us, without lots of low-level work to disentangle the different color values from each pixel value. So we convert the RGB stack to a hyperstack with two separate channels, where each channel slice is a 32-bit FloatProcessor.

The first step is to create a new ImageStack instance, to hold all the slices that we'll need: one per color channel, times the number of slices.
We ignore the blue channel (which is empty in the "Fly brain" image), so we end up creating twice as many slices as we had in the RGB stack.

Realize that we could have 7 channels if we wanted, or 20, for each slice. As many as you want.

The final step is to open the hyperstack. For that:

  1. We assign the new stack2 to a new ImagePlus, imp2.
  2. We set the same calibration (microns per pixel) that the original image has.
  3. We tell it how to interpret its image stack: as having two channels, the same amount of Z slices as before, and just 1 time frame.
  4. We pass the imp2 to a new CompositeImage, comp, indicating how we want it displayed: assign a color to each channel. (With CompositeImage.COMPOSITE, all channels would be merged for display.)
  5. We show the comp, which will open a stack window with two slides: one for the channels, and one for the Z slices.

Open the "Image - Color - Channels Tool" and you'll see that the Composite image is set to show only the red channel--try checking the second channel as well.


For a real-world example of a python script that uses hyperstacks, see the Correct_3D_drift.py script (available as the command "Plugins - Registration - Correct 3D drift").
The script takes an opened, virtual hyperstack as input, and registers in 3D every time frame to the previous one, using phase correlation, correcting any translations on the X,Y,Z axis. The script is useful for correcting sample drift under the microscope in long 4D time series.


from ij import IJ, ImagePlus, ImageStack, CompositeImage

# Load a stack of images: a fly brain, in RGB
imp = IJ.openImage("https://imagej.nih.gov/ij/images/flybrain.zip")
stack = imp.getImageStack()

# A new stack to hold the data of the hyperstack
stack2 = ImageStack(imp.width, imp.height)

# Convert each color slice in the stack
# to two 32-bit FloatProcessor slices
for i in xrange(1, imp.getNSlices()+1):
  # Get the ColorProcessor slice at index i
  cp = stack.getProcessor(i)
  # Extract the red and green channels as FloatProcessor
  red = cp.toFloat(0, None)
  green = cp.toFloat(1, None)
  # Add both to the new stack
  stack2.addSlice(None, red)
  stack2.addSlice(None, green)

# Create a new ImagePlus with the new stack
imp2 = ImagePlus("32-bit 2-channel composite", stack2)
imp2.setCalibration(imp.getCalibration().copy())

# Tell the ImagePlus to represent the slices in its stack
# in hyperstack form, and open it as a CompositeImage:
nChannels = 2             # two color channels
nSlices = stack.getSize() # the number of slices of the original stack
nFrames = 1               # only one time point 
imp2.setDimensions(nChannels, nSlices, nFrames)
comp = CompositeImage(imp2, CompositeImage.COLOR)
comp.show()
          

7. Interacting with humans: file and option dialogs, messages, progress bars.

Ask the user for a directory

See DirectoryChooser.

from ij.io import DirectoryChooser

dc = DirectoryChooser("Choose a folder")
folder = dc.getDirectory()

if folder is None:
  print "User canceled the dialog!"
else:
  print "Selected folder:", folder
          

Ask the user for a file

See OpenDialog and SaveDialog.

from ij.io import OpenDialog

od = OpenDialog("Choose a file", None)
filename = od.getFileName()

if filename is None:
  print "User canceled the dialog!"
else:
  directory = od.getDirectory()
  filepath = directory + filename
  print "Selected file path:", filepath
          

Ask the user to enter a few parameters in a dialog

There are more possibilities, but these are the basics. See GenericDialog.

All plugins that use a GenericDialog are automatable. Remember how above we run a command on an image? When the names in the dialog fields match the names in the macro string, the dialog is fed in the values automatically. If a dialog field doesn't have a match, it takes the default value as defined in the dialog declaration.

If a plugin was using a dialog like the one we built here, we would run it automatically like this:

args = "name='first' alpha=0.5 output='32-bit' scale=80"

IJ.run(imp, "Some PlugIn", args)
          

Above, leaving out the word 'optimize' means that it will use the default value (True) for it.

from ij.gui import GenericDialog

def getOptions():
  gd = GenericDialog("Options")
  gd.addStringField("name", "Untitled")
  gd.addNumericField("alpha", 0.25, 2)  # show 2 decimals
  gd.addCheckbox("optimize", True)
  types = ["8-bit", "16-bit", "32-bit"]
  gd.addChoice("output as", types, types[2])
  gd.addSlider("scale", 1, 100, 100)
  gd.showDialog()
  #
  if gd.wasCanceled():
    print "User canceled dialog!"
    return
  # Read out the options
  name = gd.getNextString()
  alpha = gd.getNextNumber()
  optimize = gd.getNextBoolean()
  output = gd.getNextChoice()
  scale = gd.getNextNumber()
  return name, alpha, optimize, output, scale

options = getOptions()
if options is not None:
  name, alpha, optimize, output, scale = options
  print name, alpha, optimize, output, scale
          

Show a progress bar

Will show a progress bar in the Fiji window.

from ij import IJ

imp = IJ.getImage()
stack = imp.getImageStack()

for i in xrange(1, stack.getSize()+1):
  # Report progress
  IJ.showProgress(i, stack.getSize()+1)
  # Do some processing
  ip = stack.getProcessor(i)
  # ...

# Signal completion
IJ.showProgress(1)
          

8. Turn your script into a plugin

Save the script in Fiji's plugins folder or a subfolder, with:

  • An underscore "_" in the name.
  • ".py" file extension.
For example: "my_script.py"

Then run "Help - Update Menus", or restart Fiji. That's it!

The script will appear as a regular menu command under "Plugins", and you'll be able to run it from the Command Finder.

Where is the plugins folder?

  • In MacOSX, it's inside the Fiji application:
    1. Go to the "Applications" folder in the Finder.
    2. Right-click on the Fiji icon and select "Show package contents"
  • In Ubuntu and in Windows, it's inside the "Fiji.app" folder.

See also the Fiji wiki on Jython Scripting.


9. Lists, native arrays, and interacting with Java classes

Jython lists are passed as read-only arrays to Java classes

Calling java classes and methods for jython is seamless: on the surface, there isn't any difference with calling jython classes and methods. But there is a subtle difference when calling java methods that expect native arrays.

Jython will automatically present a jython list as a native array to the java method that expects it. But as read-only!

In this example, we create an AffineTransform that specifies a translation. Then we give it:

  • A 2D point defined as a list of 2 numbers: the list fails to be updated in place by the transform method of the affine.
  • A 2D point defined as a native float array of 2 numbers: the array is correctly updated in place.

The ability to pass jython lists as native arrays to java methods is extremely convenient, and we have used it in the example above to pass a list of strings to the GenericDialog addChoice method.

from java.awt.geom import AffineTransform
from array import array

# A 2D point
x = 10
y = 40

# A transform that does a translation
# of dx=45, dy=56
aff = AffineTransform(1, 0, 0, 1, 45, 56)

# Create a point as a list of x,y
p = [x, y]
aff.transform(p, 0, p, 0, 1)
print p
# prints: [10, 40] -- the list p was not updated!

# Create a point as a native float array of x,y
q = array('f', [x, y])
aff.transform(q, 0, q, 0, 1)
print q
# prints: [55, 96] -- the native array q was updated properly
          
Started New_.py at Sat Nov 13 09:31:51 CET 2010
[10, 40]
array('f', [55.0, 96.0])
          

Creating native arrays: empty, or from a list

The package array contains two functions:

  • zeros: to create empty native arrays.
  • array: to create an array out of a list, or out of another array of the same kind.

The type of array is specified by the first argument. For primitive types (char, short, int, float, long, double), use a single character in quotes. See the list of all possible characters.

Manipulating arrays is done in the same way that you would do in java. See lines 16--18 in the example.

See also the documentation on how to create multidimensional native arrays with Jython.

from array import array, zeros
from ij import ImagePlus

# An empty native float array of length 5
a = zeros('f', 5)
print a

# A native float array with values 0 to 9
b = array('f', [0, 1, 2, 3, 4])
print b

# An empty native ImagePlus array of length 5
imps = zeros(ImagePlus, 5)
print imps

# Assign the current image to the first element of the array
imps[0] = IJ.getImage()
print imps

# Length of an array
print "length:", len(imps)
          
Started New_.py at Sat Nov 13 09:40:00 CET 2010
array('f', [0.0, 0.0, 0.0, 0.0, 0.0])
array('f', [0.0, 1.0, 2.0, 3.0, 4.0])
array(ij.ImagePlus, [None, None, None, None, None])
array(ij.ImagePlus, [imp[boats.gif 720x576x1], None, None, None, None])
length: 5
          

10. Generic algorithms that work on images of any kind: using ImgLib

Imglib is a general-purpose software library for n-dimensional data processing, mostly oriented towards images. Scripting with Imglib greatly simplifies operations on images of different types (8-bit, 16-bit, color images, etc).

Scripting in imglib is based around the Compute function, which composes images, functions and numbers into output images.


Mathematical operations on images

The script.imglib packages offers means to compute with images. There are three kinds of operations, each in its own package:

  • script.imglib.math: offers functions that operate on each pixel. These functions are composable: the result of one function may be used as the input to another function.
    These math functions accept any possible pair of: images, numbers, and other functions.
  • script.imglib.color: offers functions to create and manipulate color images, for example to extract specific color channels either in RGB or in HSB color space. The functions to extract channels or specific color spaces are composable with mathematical functions. For example, to subtract one color channel from another.
    These color functions are composable with math functions.
  • script.imglib.algorithm: offers functions such as Gauss, Scale3D, Affine3D, Resample, Downsample ... that alter many pixels in one pass--they are not pixel-wise operations. Some change the dimensions of an image.
    These algorithm functions all return images, or what is the same, they are the result images of applying the function to the input image.
  • script.imglib.analysis: offers functions to extract or measure images or functions that evaluate to images. For example, the DoGPeak, which finds intensity peaks in the image by difference of Gaussian, returns a list of the coordinates of the found peaks.
    These analysis functions are all collections of the results.

from script.imglib.math import Compute, Subtract
from script.imglib.color import Red, Green, Blue, RGBA
from script.imglib import ImgLib
from ij import IJ

# Open an RGB image stack
imp = IJ.openImage("https://imagej.nih.gov/ij/images/flybrain.zip")

# Wrap it as an Imglib image
img = ImgLib.wrap(imp)

# Example 1: subtract red from green channel
sub = Compute.inFloats(Subtract(Green(img), Red(img)))

ImgLib.wrap(sub).show()

# Example 2: subtract red from green channel, and compose a new RGBA image
rgb = RGBA(Red(img), Subtract(Green(img), Red(img)), Blue(img)).asImage()

ImgLib.wrap(rgb).show()
          

Using image math for flat-field correction

In the example, we start by opening an image from the sample image collection of ImageJ.
Then, since we are lacking a flatfield image, we simulate one. We could do it using a median filter with a very large radius, but that it's too expensive to compute just for this example. Instead, we scale down the image, apply a Gauss to the scaled down image, and then resample the result up to the original image dimensions.
Then we do the math for flat-field correction:

  1. Subtract the brighfield from the image. (The brighfield is an image taken in the same conditions as the data image, but without the specimen: just the dust and debris and uneven illumination of the microscope.)
  2. Subtract the darkfield from the image. (The darkfield could represent the thermal noise in the camera chip.)
  3. Divide 1 by 2.
  4. Multiply 3 by the mean intensity of the original image.

With imglib, all the above operations happen in a pixel-by-pixel basis, and are computed as fast or faster than if you had manually hand-coded every operation. And multithreaded!


from script.imglib.math import Compute, Divide, Multiply, Subtract
from script.imglib.algorithm import Gauss, Scale2D, Resample
from script.imglib import ImgLib
from ij import IJ

# 1. Open an image
img = ImgLib.wrap(IJ.openImage("https://imagej.nih.gov/ij/images/bridge.gif"))

# 2. Simulate a brighfield from a Gauss with a large radius
# (First scale down by 4x, then gauss of radius=20, then scale up)
brightfield = Resample(Gauss(Scale2D(img, 0.25), 20), img.getDimensions())

# 3. Simulate a perfect darkfield
darkfield = 0

# 4. Compute the mean pixel intensity value of the image
mean = reduce(lambda s, t: s + t.get(), img, 0) / img.size()

# 5. Correct the illumination
corrected = Compute.inFloats(Multiply(Divide(Subtract(img, brightfield),
                                             Subtract(brightfield, darkfield)), mean))

# 6. ... and show it in ImageJ
ImgLib.wrap(corrected).show()
          

Extracting and manipulating image color channels: RGBA and HSB

In the examples above we have already used the Red and Green functions. There's also Blue, Alpha, and a generic Channel that takes the channel index as argument--where red is 3, green is 2, blue is 1, and alpha is 4 (these numbers are related to the byte order in the 4-byte that makes up a 32-bit integer). The basic color operations have to do with extracting the color channel, for a particular color space (RGBA or HSB)

The function RGBA takes from 1 to 4 arguments, and creates an RGBA image out of them. These arguments can be images, other functions, or numbers--for example, all pixels of a channel would have the value 255 (maximum intensity).
In the example, we create a new RGBA image that takes the Gaussian of the red channel, the value 40 for all pixels of the green channel, and the dithered image of the blue channel.
Notice that the Dither function returns 0 or 1 values for each pixel, hence we multiply them by 255 to make them full intensity of blue in the RGBA image.


from script.imglib.math import Compute, Subtract, Multiply
from script.imglib.color import Red, Blue, RGBA
from script.imglib.algorithm import Gauss, Dither
from ij import IJ

# Obtain a color image from the ImageJ samples  
clown = ImgLib.wrap(IJ.openImage("https://imagej.nih.gov/ij/images/clown.jpg"))
  
# Example 1: compose a new image manipulating the color channels of the clown image:  
img = RGBA(Gauss(Red(clown), 10), 40, Multiply(255, Dither(Blue(clown)))).asImage()  
  
ImgLib.wrap(img).show()
          

In the second example, we extract the HSB channels from the clown image. To the Hue channel (which is expressed in the range [0, 1]), we add 0.5. We've shifted the hue around a bit.
To understand how the hue values work (by flooring the float value and subtracting that from it), see this page.

 


from script.imglib.math import Compute, Add, Subtract
from script.imglib.color import HSB, Hue, Saturation, Brightness
from script.imglib import ImgLib
from ij import IJ

# Obtain an image
img = ImgLib.wrap(IJ.openImage("https://imagej.nih.gov/ij/images/clown.jpg"))

# Obtain a new clown, whose hue has been shifted by half
# with the same saturation and brightness of the original
bluey = Compute.inRGBA(HSB(Add(Hue(img), 0.5), Saturation(img), Brightness(img)))

ImgLib.wrap(bluey).show()
          

In the third example, we apply a gamma correction to an RGB confocal stack. To correct the gamma, we must first extract each color channel from the image, and then apply the gamma to each channel independently. In this example we use a gamma of 0.5 for every channel. Of course you could apply different gamma values to each channel, or apply it only to specific channels.

Notice how we use asImage() instead of Compute.inRGBA. The result is the same; the former is syntactic sugar of the latter.

 


# Correct gamma
from script.imglib.math import Min, Max, Exp, Multiply, Divide, Log
from script.imglib.color import RGBA, Red, Green, Blue
from ij import IJ

gamma = 0.5
img = ImgLib.wrap(IJ.getImage())

def g(channel, gamma):
  """ Return a function that, when evaluated, computes the gamma
	    of the given color channel.
      If 'i' was the pixel value, then this function would do:
      double v = Math.exp(Math.log(i/255.0) * gamma) * 255.0);
      if (v < 0) v = 0;
      if (v >255) v = 255;
  """
  return Min(255, Max(0, Multiply(Exp(Multiply(gamma, Log(Divide(channel, 255)))), 255)))

corrected = RGBA(g(Red(img), gamma), g(Green(img), gamma), g(Blue(img), gamma)).asImage()

ImgLib.wrap(corrected).show()
          

Find cells in an 3D image stack by Difference of Gaussian, count them, and show them in 3D as spheres.

First we define the cell diameter that we are looking for (5 microns; measure it with a line ROI over the image) and the minimum voxel intensity that will care about (in this case, anything under a value of 40 will be ignored). And we load the image of interest: a 3-color channel image of the first instar Drosophila larval brain.

Then we scale down the image to make it isotropic: so that voxels have the same dimensions in all axes.

We run the DoGPeaks ("Difference of Gaussian Peaks") with a pair of appropriate sigmas: the scaled diameter of the cell, and half that.

The peaks are each a float[] array that specifies its coordinate. With these, we create Point3f instances, which we transport back to calibrated image coordinates.

Finally, we show in the 3D Viewer the peaks as spheres, and the image as a 3D volume.


 
# 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 ij3d import Image3DUniverse
from javax.vecmath import Color3f, Point3f
from ij import IJ

cell_diameter = 5  # in microns
minPeak = 40 # The minimum intensity for a peak to be considered so.
imp = IJ.openImage("http://samples.fiji.sc//samples/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"

# Convert the peaks into points in calibrated image space
ps = []
for peak in peaks:
  p = Point3f(peak)
  p.scale(cal.pixelWidth * 1/scale2D)
  ps.append(p)

# Show the peaks as spheres in 3D, along with orthoslices:
univ = Image3DUniverse(512, 512)
univ.addIcospheres(ps, Color3f(1, 0, 0), 2, cell_diameter/2, "Cells").setLocked(True)
univ.addOrthoslice(imp).setLocked(True)
univ.show()
          

11. ImgLib2: writing generic, high-performance image processing programs

For a high-level introduction to ImgLib2, see:

Views of an image, with ImgLib2

ImgLib2 is a powerful library with a number of key concepts for high-performance, memory-efficient image processing. One such concept is that of a view of an image.

First, wrap a regular ImageJ ImagePlus into an ImgLib2 image, with the 'wrap' function in the ImageJFunctions namespace (AKA a static method), which we alias as IL for brevity using the as keyword in the import line.

Then, we view the image as an infinite image, using the Views.extendZero function: beyond the boundaries of the image, return the value zero as the pixel value.

An infinite image cannot be visualized in full. Therefore, we apply the Views.interval function to delimit it: in this example, to a "canvas" twice as large as before, with the image centered.

Then, we wrap the ImgLib2 interval imgL into an ImageJ's ImagePlus (using a modified VirtualStack that reads directly from the imgL), and show it.

Importantly, no pixel data was duplicated at any step. The Views concept enables us to define transformations to the image that are then concatenated and finally used to render the final image.

And furthermore, thanks to ImgLib2's underlying dimension-independent, data source-independent, and image type-independent model, this code applies to any image of any type and dimensions: images, volumes, 4D series. ImgLib2 is a very powerful library.

from ij import IJ
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from net.imglib2.view import Views

# Load an image (of any dimensions) such as the clown sample image
imp = IJ.getImage()

# Convert to 8-bit if it isn't yet, using macros
IJ.run(imp, "8-bit", "")

# Access its pixel data from an ImgLib2 RandomAccessibleInterval
img = IL.wrapReal(imp)

# View as an infinite image, with a value of zero beyond the image edges
imgE = Views.extendZero(img)

# Limit the infinite image with an interval twice as large as the original,
# so that the original image remains at the center.
# It starts at minus half the image width, and ends at 1.5x the image width.
minC = [int(-0.5 * img.dimension(i)) for i in range(img.numDimensions())]
maxC = [int( 1.5 * img.dimension(i)) for i in range(img.numDimensions())]
imgL = Views.interval(imgE, minC, maxC)

# Visualize the enlarged canvas, so to speak
imp2 = IL.wrap(imgL, imp.getTitle() + " - enlarged canvas") # an ImagePlus
imp2.show()
          


There are multiple strategies for filling in the space beyond an image boundaries. Above, we used Views.extendZero, which trivally sets the "outside" to the pixel value zero. But there are several variants, including View.extendValue for arbitrary pixel values instead of zero; Views.extendMirrorSingle and Views.extendMirrorDouble for mirroring the pixel values relative to the nearest image border, and others. See Views for details and for more.

In this example, we use Views.extendMirrorSingle and the effect is clear when we take an interval over it just like the one above: instead of the image surrounded by black space, we get mirror copies in every direction beyond the edges of the original image, which remains centered.

The various extended views each have their purpose. Extending enables, for example, to avoid writing in special purpose code for e.g. algorithms that use a moving window around every pixel. The pixels on the border or near the border (depending on the size of the window) would need to be special cased. Instead, with extended views, you can specify what data should be present beyond the border (a constant value, a mirror reflection of the image), and reduce enormously the complexity of your code.

You could also use them like ROIs (regions of interest): obtain a View on a specific region of the image, and apply to it any code that runs on whole images. Views simplify programming for image processing a lot.


img = ... # See above

# View mirroring the data beyond the edges
imgE = Views.extendMirrorSingle(img)
imgL = Views.interval(imgE, minC, maxC)

# Visualize the enlarged canvas, so to speak
imp2 = IL.wrap(imgL, imp.getTitle() + " - enlarged canvas") # an ImagePlus
imp2.show()
					


Difference of Gaussian peak detection with ImgLib2, and using View.interval as an ROI.

First we load ImageJ's "embryos" example image, which is RGB, and convert it to 8-bit (16-bit or 32-bit would work just fine). Then we wrap it as an ImgLib2 image, and acquire a mirroring infinite view of the image which is suitable for computing Gaussians.

The parameters of ImgLib2's Difference of Gaussian detection (DogDetection) are relatively straightforward. The key parameters are the sigmaLarger and sigmaSmaller, which define the sigmas of the two Gaussians that will be subtracted one from the other. The minPeakValue acts as a filter for noisy detections. The calibration would be useful in e.g. an LSM 3D volume where the Z axis has typically a lower resolution than the X and Y axes.

For visual validation, we read out the detected peaks as a PointRoi that we set on the imp, the original ImagePlus with the embryos (see image below with a PointRoi point on each embryo).

Then, we set out to measure a small interval around each detected peak (each embryo). For this, we use the sigmaSmaller, which is half of the radius of an embryo (determined empirically by using a line ROI over embryos and pushing 'm' to measure them), so that we define a 2d box around the peak, with a side twice that of sigmaSmaller plus one. Ideally, one would use a circular ROI by using a HyperSphere, but a square ROI as obtained with a View.interval will more than suffice here.

To sum the pixel intensity values within the interval, we use Views.flatIterable on the interval, which provides a view that can be serially iterated over the interval. Otherwise, the interval, which is a RandomAccessibleInterval, would yield its pixel values only if we gave it each pixel coordinate to be measured. Then, we iterate each small view, obtaining a t (a Type) instance for every pixel, which in ImgLib2 is one of the key design features that enables so much indirection without sacrificing performance. To the t Type, which is a subclass of NumericType, we ask it to yield an integer with t.getInteger(). Python's built-in sum function adds up all the values of the generator (no list is created).

Finally, the peak X,Y coordinates and the sum of pixel values within the interval are added to an ImageJ ResultsTable.

 


from ij import IJ
from ij.gui import PointRoi
from ij.measure import ResultsTable
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from net.imglib2.view import Views
from net.imglib2.algorithm.dog import DogDetection
from jarray import zeros

# Load a greyscale single-channel image: the "Embryos" sample image
imp = IJ.openImage("https://imagej.nih.gov/ij/images/embryos.jpg")
# Convert it to 8-bit
IJ.run(imp, "8-bit", "")

# Access its pixel data from an ImgLib2 data structure: a RandomAccessibleInterval
img = IL.wrapReal(imp)

# View as an infinite image, mirrored at the edges which is ideal for Gaussians
imgE = Views.extendMirrorSingle(img)

# Parameters for a Difference of Gaussian to detect embryo positions
calibration = [1.0 for i in range(img.numDimensions())] # no calibration: identity
sigmaSmaller = 15 # in pixels: a quarter of the radius of an embryo
sigmaLarger = 30  # pixels: half the radius of an embryo
extremaType = DogDetection.ExtremaType.MAXIMA
minPeakValue = 10
normalizedMinPeakValue = False

# In the differece of gaussian peak detection, the img acts as the interval
# within which to look for peaks. The processing is done on the infinite imgE.
dog = DogDetection(imgE, img, calibration, sigmaSmaller, sigmaLarger,
  extremaType, minPeakValue, normalizedMinPeakValue)

peaks = dog.getPeaks()

# Create a PointRoi from the DoG peaks, for visualization
roi = PointRoi(0, 0)
# A temporary array of integers, one per dimension the image has
p = zeros(img.numDimensions(), 'i')
# Load every peak as a point in the PointRoi
for peak in peaks:
  # Read peak coordinates into an array of integers
  peak.localize(p)
  roi.addPoint(imp, p[0], p[1])

imp.setRoi(roi)

# Now, iterate each peak, defining a small interval centered at each peak,
# and measure the sum of total pixel intensity,
# and display the results in an ImageJ ResultTable.
table = ResultsTable()

for peak in peaks:
  # Read peak coordinates into an array of integers
  peak.localize(p)
  # Define limits of the interval around the peak:
  # (sigmaSmaller is half the radius of the embryo)
  minC = [p[i] - sigmaSmaller for i in range(img.numDimensions())]
  maxC = [p[i] + sigmaSmaller for i in range(img.numDimensions())]
  # View the interval around the peak, as a flat iterable (like an array)
  fov = Views.flatIterable(Views.interval(img, minC, maxC))
  # Compute sum of pixel intensity values of the interval
  # (The t is the Type that mediates access to the pixels, via its get* methods)
  s = sum(t.getInteger() for t in fov)
  # Add to results table
  table.incrementCounter()
  table.addValue("x", p[0])
  table.addValue("y", p[1])
  table.addValue("sum", s)

table.show("Embryo intensities at peaks")
					


Transform an image using ImgLib2.

In this example, we will use ImgLib2's RealViews namespace to transform images with affine transforms: translate, rotate, scale, shear.

Let's introduce the concept of a View in ImgLib2: it's like a shallow copy, possibly transformed. Meaning, the underlying pixel array is not duplicated, with merely a transformation of some sort being applied to the pixels on the fly as these are requested. Views can be concatenated.

Here we use:

  • Views.extendZero: takes a finite image and returns a view that returns the proper pixel values within the image, but a pixel value of zero beyond its edges.
  • Views.interpolate: enables retrieving pixel values for fractional coordinates (i.e. non-integer coordinates) with the help of an interpolation strategy, such as the NLinearInterpolatorFactory. Returns images of the RealRandomAccessible type, suitable for transformations.
  • RealViews.transform: views an image as transformed by the provided transformation, such as an affine transform. Operates on images that are RealRandomAccessible, such as those returned by Views.interpolate.
  • Views.interval: takes an infinite image (generally an infinite View) and adds limits to it, defining specific intervals in each of its dimensions within which the image is said to be defined. This is what we use to "crop" or to select a specific field of view. If the field of view includes regions outside the originally wrapped image, then it'd better be "filled in" with a Views.extend (like Views.extendZero) or it will fail with out of bounds exception when a user of the returned interval attemps to get pixels from such "outside" regions.

While the reasons that led to split the functionality into two separate namespaces (the Views and the RealViews) don't matter, the basic heuristic when looking up for a View method is that we'll use Views when the interval is defined (that is, the image data is known to exist within a specific range between 0 and width, height, depth, etc., which is almost always), and we'll use RealViews when the interval is not defined and pixels can be retrieved with real numbers, that is, floating point numbers (such as when applying affine transforms or performing interpolations).

In the end, we call ImageJFunctions.wrap again to visualize the transformed image as a regular ImageJ's ImagePlus containing a VirtualStack whose pixel source is the scaled up View, whose pixel source, in turn, is the original ImagePlus. No data has been duplicated at any step!

from net.imglib2.realtransform import RealViews as RV
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from net.imglib2.realtransform import Scale
from net.imglib2.view import Views
from net.imglib2.interpolation.randomaccess import NLinearInterpolatorFactory
from ij import IJ

# Load an image (of any dimensions)
imp = IJ.getImage()

# Access its pixel data as an ImgLib2 RandomAccessibleInterval
img = IL.wrapReal(imp)

# View as an infinite image, with a value of zero beyond the image edges
imgE = Views.extendZero(img)

# View the pixel data as a RealRandomAccessible
# (that is, accessible with sub-pixel precision)
# by using an interpolator
imgR = Views.interpolate(imgE, NLinearInterpolatorFactory())

# Obtain a view of the 2D image twice as big
s = [2.0 for d in range(img.numDimensions())] # as many 2.0 as image dimensions
bigger = RV.transform(imgR, Scale(s))

# Define the interval we want to see: the original image, enlarged by 2X
# E.g. from 0 to 2*width, from 0 to 2*height, etc. for every dimension
minC = [0 for d in range(img.numDimensions())]
maxC = [int(img.dimension(i) * scale) for i, scale in enumerate(s)]
imgI = Views.interval(bigger, minC, maxC)

# Visualize the bigger view
imp2x = IL.wrap(imgI, imp.getTitle() + " - 2X") # an ImagePlus
imp2x.show()
          

 


At any time, use e.g. print type(imgR) to see the class of e.g. the object imgR. Then, either look it up in the ImgLib2's github repositories or in Google, or perhaps sufficiently, use print dir(imgR) to list all its accessible methods.


While the code in this example applies to images of any number of dimensions (2D, 3D, 4D) and type (8-bit, 16-bit, 32-bit, others), here we scale by a factor of two the boats example ImageJ image.


print type(imgR)
print dir(imgR)
					

<type 'net.imglib2.interpolation.Interpolant'>

['__class__', '__copy__', '__deepcopy__', '__delattr__', '__doc__',
'__ensure_finalizer__', '__eq__', '__format__', '__getattribute__',
'__hash__', '__init__', '__ne__', '__new__', '__reduce__', '__reduce_ex__',
'__repr__', '__setattr__', '__str__', '__subclasshook__', '__unicode__',
'class', 'equals', 'getClass', 'getInterpolatorFactory', 'getSource',
'hashCode', 'interpolatorFactory', 'notify', 'notifyAll', 'numDimensions',
'realRandomAccess', 'source', 'toString', 'wait']
					

The resulting ImagePlus can be saved using ImageJ's FileSaver methods, just like any other ImageJ image.

from ij.io import FileSaver

FileSaver(imp2x).saveAsPng("/path/to/boats-2x.png")
					

Rotating image volumes with ImgLib2.

Now we continue with a rotation around the Z axis (rotation in XY) by 30 degrees. Remember, this code applies to images of any number of dimensions: would work equally well as is for the boats image example above.

The rotation must be defined as the values of a matrix that describes an affine transform. For convenience, I use here the java.awt.geom.AffineTransform (aliased as Affine2D) to obtain the values of the rotation transform. Then these are transferred to a JaMa Matrix, which the ImgLib2's AffineTransform class takes as argument for its constructor. The matrix has to have one more column than rows, with the last column defining the translation. (The last row would be all zeros and a 1.0 at the end, so it is omitted.) Notice that the rest of the diagonal of the matrix is filled with 1.0 in the loop, for as many dimensions as the image has.

Then we view the rotated image as an ImagePlus that wraps a VirtualStack just like above. Of course, the rotated image is cropped: when rotating relative to the center, the center stays within the field of view, but the corners disappear. Below, we instead view an enlarged interval that fully contains the rotated image. (In this particular example the effect is not very visible because the MRI stack of a human head has black corners. To reveal the issue, I draw a white line along the borders beforehand by pushing 'a' to select all with a rectangular ROI, then choosing white color for the foreground color, and then pushing 'd' to draw it, confirming the dialog to draw in every section.)

 

from net.imglib2.realtransform import RealViews as RV
from net.imglib2.realtransform import AffineTransform
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from ij import IJ
from net.imglib2.view import Views
from net.imglib2.interpolation.randomaccess import NLinearInterpolatorFactory
from java.awt.geom import AffineTransform as Affine2D
from java.awt import Rectangle
from Jama import Matrix
from math import radians

# Load an image (of any dimensions)
imp = IJ.getImage()

# Access its pixel data as an ImgLib2 RandomAccessibleInterval
img = IL.wrapReal(imp)

# View as an infinite image, with value zero beyond the image edges
imgE = Views.extendZero(img)

# View the pixel data as a RealRandomAccessible
# (that is, accessible with sub-pixel precision)
# by using an interpolator
imgR = Views.interpolate(imgE, NLinearInterpolatorFactory())

# Define a rotation by +30 degrees relative to the image center in the XY axes
# (not explicitly XY but the first two dimensions)
# by filling in a rotation matrix with values taken
# from a java.awt.geom.AffineTransform (aliased as Affine2D)
# and by filling in the rest of the diagonal with 1.0
# (for the case where the image has more than 2 dimensions)
angle = radians(30)
rot2d = Affine2D.getRotateInstance(
  angle, img.dimension(0) / 2, img.dimension(1) / 2)
ndims = img.numDimensions()
matrix = Matrix(ndims, ndims + 1)
matrix.set(0, 0, rot2d.getScaleX())
matrix.set(0, 1, rot2d.getShearX())
matrix.set(0, ndims, rot2d.getTranslateX())
matrix.set(1, 0, rot2d.getShearY())
matrix.set(1, 1, rot2d.getScaleY())
matrix.set(1, ndims, rot2d.getTranslateY())
for i in range(2, img.numDimensions()):
  matrix.set(i, i, 1.0)

print matrix.getArray()

# Define a rotated view of the image
rotated = RV.transform(imgR, AffineTransform(matrix))

# View the image rotated, without enlarging the canvas
# so we define the interval as the original image dimensions.
# (Notice the -1 on the max coordinate: the interval is inclusive)
minC = [0 for i in range(img.numDimensions())]
maxC = [img.dimension(i) -1 for i in range(img.numDimensions())]
imgRot2d = IL.wrap(Views.interval(rotated, minC, maxC),
  imp.getTitle() + " - rot2d")
imgRot2d.show()

# View the image rotated, enlarging the interval to fit it.
# (This is akin to enlarging the canvas.)
# We compute the bounds of the enlarged canvas by transforming a rectangle,
# then define the interval min and max coordinates by subtracting
# and adding as appropriate to exactly capture the complete rotated image.
# Notice the min coordinates have negative values, as the rotated image
# has pixels now somewhere to the left and up from the top-left 0,0 origin
# of coordinates.
bounds = rot2d.createTransformedShape(
  Rectangle(img.dimension(0), img.dimension(1))).getBounds()
minC[0] = (img.dimension(0) - bounds.width) / 2
minC[1] = (img.dimension(1) - bounds.height) / 2
maxC[0] += abs(minC[0]) -1 # -1 because its inclusive
maxC[1] += abs(minC[1]) -1
imgRot2dFit = IL.wrap(Views.interval(rotated, minC, maxC),
  imp.getTitle() + " - rot2dFit")
imgRot2dFit.show()
					

To read out the values of the transformation matrix that specifies the rotation, print it: it's an array of arrays. Or pretty-print it with pprint, which requires turning the inner arrays into lists for nicer printing.

Given the desired 30 degree rotation, the "scale" part (the diagonal) becomes the cosine of 30 degrees (sqrt(3)/2 = 0.866), and the "shear" part (the second column of the first row, and the first column of the second row) becomes the sine of 30 degrees (0.5) with the appropriate sign (to the "left" for X, hence negative; and to the "right" for Y, hence positive). The third column contains the translation values corresponding to a rotation specified relative to the center of the image. While you could always write in the matrix by hand, it is better to use libraries like, for 2D, the java.awt.geom.AffineTransform and its methods such as getRotateInstance. For 3D rotations and affine transformations in general, use e.g. javax.media.j3d.Transform3D and e.g. its method rotZ, which sets the transform to mean a rotation in the Z axis, as done in this example.


print matrix.getArray()

from pprint import pprint
pprint([list(row) for row in matrix.getArray()])
					

array([D, [array('d', [0.8660254037844387, -0.49999999999999994, 0.0,
68.95963744804719]), array('d', [0.49999999999999994, 0.8660254037844387,
0.0, -31.360870627641567]), array('d', [0.0, 0.0, 1.0, 0.0])])

[[0.8660254037844387,  -0.49999999999999994, 0.0,  68.95963744804719],
 [0.49999999999999994,  0.8660254037844387,  0.0, -31.360870627641567],
 [0.0,                  0.0,                 1.0,  0.0]]
					

Processing RGB and ARGB images with ImgLib2.

An ARGB image is a hack: the four color channels have been stored each in one of the 4 bytes of a 32-bit integer. Processing directly the pixel array, made of integers, makes no sense at all. Prior to any processing, color channels must be separated.

For reference, the alpha channel is in the upper byte (index 0), the red in the 2nd (index 1), the green in the 3rd (index 2) and blue in the lowest byte, the 4th (index 3).

In ImgLib2, rather than copying a color channel into a new image with a new array of bytes, we acquire a View of its channels: by using the Converters functions, optionally together with the Views.hyperSlice functionality.

First, we load an RGB or ARGB image and wrap it as an ImgLib2 object (despite what IL.wrapRGBA seems to imply, the alpha channel is still at index 0). If the ImagePlus is not backed by a ColorProcessor, it will throw an error.

Then we invoke one of the several functions in the Converters namespace that handles ARGB images. Here, we use Converters.argbChannels, which delivers a view of the ARGB image as a stack of 4 images, one per channel. The channels image is equivalent to ImageJ's CompositeImage, in that each channel can be processed independently.

To read out a single channel, e.g. the red channel (index 1), we could use Converters.argbChannel(img, 1). Or, as we illustrate here, use Views.hyperSlice: a function to reduce the dimensionally of an image, in this case by fixing the last dimension (the channels) to always be the red channel (at index 1).

Of course, this code runs on 2D images (e.g. the leaf) or 3D images (e.g. the Drosophila larval brain LSM stack), or 4D images, or images of any dimensions.

from net.imglib2.converter import Converters
from net.imglib2.view import Views
from net.imglib2.img.display.imagej import ImageJFunctions as IL
from ij import IJ

# # Load an RGB or ARGB image
imp = IJ.getImage()

# Access its pixel data from an ImgLib2 data structure:
# a RandomAccessibleInterval
img = IL.wrapRGBA(imp)

# Convert an ARGB image to a stack of 4 channels: a RandomAccessibleInterval
# with one more dimension that before.
# The order of channels in the stack can be changed by changing their indices.
channels = Converters.argbChannels(img, [0, 1, 2, 3])

# Equivalent to ImageJ's CompositeImage: channels are separate
impChannels = IL.wrap(channels, imp.getTitle() + " channels")
impChannels.show()

# Read out a single channel directly
red = Converters.argbChannel(img, 1)

# Alternatively, pick a view of the red channel in the channels stack.
# Takes the last dimension, which are the channels,
# and fixes it to just one: that of the red channel (1) in the stack.
red = Views.hyperSlice(channels, channels.numDimensions() -1, 1)

impRed = IL.wrap(red, imp.getTitle() + " red channel")
impRed.show()