Multithreaded Image Processing in JavaScript

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Scripting

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Multithreaded Processing

Examples

Purpose

An example Javascript ImageJ script illustrating how to create java Threads for concurrent execution.

The example illustrates as well the use of functions with variable arguments.

Code

// Albert Cardona 20081109
// This code is released under the public domain
//
// A multithreading framework for ImageJ in javascript
//
// First a function named doItMultithreaded shows how to run a process in
// parallel, in as many threads as CPU cores. The key idea is that of iterating
// over a list of numbers from start to end, where each index in the list means
// something: for example, a line of pixels in an image.
//
// The example simply prints a list of numbers in a multithreaded way.
//
// Then, the multithreading part and the printing part are separated into the
// "multithreader" function and the "printer" function. The "printer" is
// invoked by passing it to the "multithreader" function as an argument.
//
// Finally, a more real-world example is show, in which lines of an image, 10
// lines at a time, are processed independelty in separate threads and filled
// with random pixel values, using the "multithreader" framework function.
//
// Please note that generating random values has so little overhead that a
// multithreaded setup does not pay off for small images, no matter how many
// lines at a time are processed together. This is intended as an example of
// what could be done, for example, for very computationally expensive filters
// like a large median filter or a gaussian with a large standard deviation.
//
// Have fun!

importClass(Packages.java.util.concurrent.atomic.AtomicInteger);

// Print all numbers from start to end (inclusive), multithreaded
function doItMultithreaded(start, end) {
	var threads = new java.lang.reflect.Array.newInstance(java.lang.Thread, Runtime.getRuntime().availableProcessors());
	var ai = new AtomicInteger(start);
	var body = {
		run: function() {
			for (var i = ai.getAndIncrement(); i <= end; i = ai.getAndIncrement()) {
				IJ.log("i is " + i);
				Thread.sleep(100); // NOT NEEDED, just to pretend we are doing something!
			}
		}
	}
	// start all threads
	for (var i = 0; i < threads.length; i++) {
		threads[i] = new Thread(new Runnable(body)); // automatically as Runnable
		threads[i].start();
	}
	// wait until all threads finish
	for (var i = 0; i < threads.length; i++) {
		threads[i].join();
	}
}

// execute like:
// doItMultithreaded(0, 10);

// Now, abstract away the multithreading framework into a function
// that takes another function as argument:
function multithreader(fun, start, end) {
	var threads = new java.lang.reflect.Array.newInstance(java.lang.Thread, Runtime.getRuntime().availableProcessors());
	var ai = new AtomicInteger(start);
	// Prepare arguments: all other arguments passed to this function
	// beyond the mandatory arguments fun, start and end:
	var args = new Array();
	var b = 0;
	IJ.log("Multithreading function \"" + fun.name + "\" with arguments:\n  argument 0 is index from " + start + " to " + end);
	for (var a = 3; a < arguments.length; a++) {
		args[b] = arguments[a];
		IJ.log("  argument " + (b+1) + " is " + args[b]);
		b++;
	}
	var body = {
		run: function() {
			for (var i = ai.getAndIncrement(); i <= end; i = ai.getAndIncrement()) {
				// Execute the function given as argument,
				// passing to it all optional arguments:
				fun(i, args);
			}
		}
	}
	// start all threads
	for (var i = 0; i < threads.length; i++) {
		threads[i] = new Thread(new Runnable(body)); // automatically as Runnable
		threads[i].start();
	}
	// wait until all threads finish
	for (var i = 0; i < threads.length; i++) {
		threads[i].join();
	}
}

// The actual desired effect: the printer
function printer(i) {
	IJ.log("i is " + i);
}

// Execute like (uncomment!):
// multithreader(printer, 0, 10);


// Above, notice how we are passing the printer function as an argument to the
// multithreader function, which is executed simply by putting parenthesis to
// its name. Simple!


// Now, armed with the multithreader, we can parallelize any function we want:
// for example, filling each pixel of an image with a random value.
//
// The key for best performance is to break down the task in significant
// chunks. Multithreading for each pixel makes little sense--to much overhead
// wipes away the gain. Multithreading for one line, same thing: a random value
// is not so costly to compute, still too much overhead. So we are going to
// multithread the processing of for example 100 lines at a time:


// Takes a starting line and a number of lines to process,
// and sets their pixels to a random value
function randomizer(line, args) {
	// Obtain and check the arguments:
	if (args.length < 5) {
		IJ.log("randomizer needs at least 5 arguments: line, pix, width, height, n_lines and rand");
		return;
	}
	var pix = args[0];
	var width = args[1];
	var height = args[2];
	var n_lines = args[3];
	var rand = args[4];
	for (var y = line; y < height && y < height + n_lines; y++) {
		var offset = y * width;
		for (var x = 0; x < width; x++) {
			pix[offset + x] = rand.nextFloat();
		}
	}
}

// Test: create a new image, fill it with random values, and show it
width = 512;
height = 512;
imp = new ImagePlus("Random", new FloatProcessor(width, height));
pix = imp.getProcessor().getPixels();
importClass(Packages.java.util.Random);
rand = new Random(System.currentTimeMillis());
block_size = 100; // number of lines to be processed together
n_blocks = ((height / block_size)|0) + 1; // casting to int with bitwise or to zero

// Execute the randomizer in multithreaded fashion:
//   - At the top row, the three arguments for the multithreading framework
//   - At the bottom row, the N arguments for the function to parallelize
multithreader(randomizer, 0, n_blocks,
              pix, width, height, block_size, rand);

// Show the image:
imp.getProcessor().setMinAndMax(0, 1); // random values between 0 and 1
imp.show();


See also