Not long ago, I read this nice little blog entry about the basic properties and usages of Java arrays. It’s a long time since I last used an array in Java myself, because my programming style evolved to heavily leverage the power of collections (and Iterables in particular, the Java 5 poor man’s substitute for Java 8 Streams). But I immediately noticed that one important fact was missing from the array blog entry:
The maximum length of an array in Java is Integer.MAX_VALUE or ((2^32)-1), aka 2.147.483.647
This is indirectly specified in the Java language specification, chapter 10.4 Array Access:
Arrays must be indexed by int values.
This little fact crossed my path when writing a little tool in pure Java that operated on large numbers of large images, combining them to a gigantic image. The customer used the tool to create images that had a size of about 100 MB, but took several hours to print because the decompression tax kicked in. One day, he reported a strange bug:
“Oh, a negative array size, what a strange bug to appear in a tested application” was my first thought. Only after reading the stacktrace more carefully did it dawn on me: The array size wasn’t negative, it was just bigger than Integer.MAX_VALUE and got wrapped around into the negative numbers. And sure enough, 72350 times 44914 is a respectable 3.249.527.900 pixels, more than 1,5 times as much as an array in Java can hold. This image was right in the multi-gigapixel range where all kinds of technical obstacles appear. The maximum length of an array in Java was mine.
Trying to stay pure
One cornerstone of the tool was being lightweight. It shouldn’t carry around unnecessary luggage and weighted around 200 kB when the bug appeared – enough to just copy it into the data directories instead of pulling the directories into the program. But when I examined the root cause of the problem at hand, I found the frustrating truth that Java’s built-in imaging library also relies on one cornerstone: all data is stored in one array. And this array can only hold around 2G entries of data.
My approach was to “partition” the full image into smaller parts that only stored a fraction of the overall pixels. To hide this fact from the ImageIO that ultimatively writes all the data into one file, my PartitionedImage implements RenderedImage and has to translate every call into a series of appropriate subcalls to the partition images. Before we look at some code, let me show you the limitations of this approach:
Greedy JPEGs, credulous PNGs
In the RenderedImage interface, there are two methods that can be used to obtain pixel data:
- Raster getData(): Returns the image as one large tile (for tile based images this will require fetching the whole image and copying the image data over).
- Raster getData(Rectangle rect): Computes and returns an arbitrary region of the RenderedImage.
If an image writer calls the first method, my code is screwed. There is no mentally sane way to construct a Raster instance without colliding with the array length limitation. Unfortunately, the JPEG writer does just that: He gets greedy and demands all the pixels at once. I found it easier to avoid the JPEG format and therefore trade disk space for pragmatism.
The PNG writer uses the getData(Rectangle) method to obtain the pixel data. It calls the whole image line by line: the region has always the full width of the image, but is only one pixel in height. So I guess my tool will write a lot of large PNG images in the future.
Our partitions should adapt to this behaviour by always retaining the full width of the original image and only allowing enough height that the amount of pixels per partition doesn’t exceed Integer.MAX_VALUE.
The remaining trick is to implement an AdjustingRaster that knows the original Raster of the partition and translates the row asked by the PNG writer to the according row in the partition image. The AdjustingRaster needs to know about the vertical offset. The only pitfall here is that the vertical offset has to be zero while the AdjustingRaster gets written to and needs to be set once it switches into read mode.
Slow, but working
By composing a gigapixel image from several partitions (sometimes called tiles) you can circumnavigate the frustrating limitation of Java’s arrays (I mean, it’s 2014 and 64-bit systems are somewhat prevailing now. No need to stick to 32-bit limits without a good reason). The result isn’t overwhelmingly fast, but I suspect that’s caused by the PNG image writer more than by our indirections. And we shouldn’t forget that it’s a lot of pixels to write after all.
Sometimes when you explore bigger and bigger use cases, you hit some arbitrary limitation. And some are fundamental ones. In our case here, we’ve reached the limit of Java arrays and got stuck because the image library in Java never heard of real gigapixel imaging and coupled itself hard to the array limit. By introducing another indirection layer on top of the image library implementation and using composition to emulate a bigger image than we actually could create, we can convince non-sceptical image writers to save all those pixels for us and even manipulate the image beforehand.
What was your approach for gigapixel image processing? How did it work out in the long run? Share your story in the comments, please.
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